Single cell seq after sorting for PhenoID

sample1 = Neurons1 sample2 = Neurons2 sample3 = Glia1 - Astrocytes (CD44+) sample4 = Glia2 - Radial Glia (CD44-)

In HPC I have run steps of scrnabox (custom pipeline in progress) 1. Cell Ranger for feature seq 2. Create Seurat Objects 3. Apply minimum filtering and calculate percent mitochondria.

I have technical 3 replicates with hashtag labels at this point I haven’t yet demultiplex the hashtags. The data here will be treated as one sample. I sorted three separate samples and pooled them together.

# set up the environment

library(Seurat)
Attaching SeuratObject
Attaching sp
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
library(Matrix)
library(ggplot2)

#rm(list = ls())

Read in the seurat objects made in compute canada

Glia2
An object of class Seurat 
33541 features across 10338 samples within 2 assays 
Active assay: RNA (33538 features, 0 variable features)
 1 other assay present: HTO

Have a look at the objects that already have some filtering

See the violin plots


VlnPlot(Neurons1, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)


VlnPlot(Neurons1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)
Warning: Removed 873 rows containing non-finite values (stat_ydensity).
Warning: Removed 873 rows containing missing values (geom_point).

VlnPlot(Neurons1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)
Warning: Removed 546 rows containing non-finite values (stat_ydensity).
Warning: Removed 546 rows containing missing values (geom_point).

# filter more cells

Neuron1.ft <- subset(Neurons1, subset = nFeature_RNA > 250 & nCount_RNA > 250 & nCount_RNA < 10000) 
Neuron1.ft
An object of class Seurat 
33541 features across 1833 samples within 2 assays 
Active assay: RNA (33538 features, 0 variable features)
 1 other assay present: HTO
# 33541 features across 1833 samples

Neurons 2 - CD56++


VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)
Warning: Removed 5653 rows containing non-finite values (stat_ydensity).
Warning: Removed 5653 rows containing missing values (geom_point).

VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
Warning: Removed 2379 rows containing non-finite values (stat_ydensity).
Warning: Removed 2379 rows containing missing values (geom_point).

VlnPlot(Neurons2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)
Warning: Removed 2264 rows containing non-finite values (stat_ydensity).
Warning: Removed 2264 rows containing missing values (geom_point).

# filter more cells

Neuron2.ft <- subset(Neurons2, subset = nFeature_RNA > 500 & nCount_RNA > 500 & nCount_RNA < 10000) 
Neuron2.ft
An object of class Seurat 
33541 features across 5190 samples within 2 assays 
Active assay: RNA (33538 features, 0 variable features)
 1 other assay present: HTO

Glia1 - Astrocyte data


VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)


VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 5000)
Warning: Removed 82 rows containing non-finite values (stat_ydensity).
Warning: Removed 82 rows containing missing values (geom_point).

VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
Warning: Removed 11811 rows containing non-finite values (stat_ydensity).
Warning: Removed 11811 rows containing missing values (geom_point).

VlnPlot(Glia1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 1000)
Warning: Removed 25751 rows containing non-finite values (stat_ydensity).
Warning: Removed 25751 rows containing missing values (geom_point).

VlnPlot(Glia1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 12000)
Warning: Removed 252 rows containing non-finite values (stat_ydensity).
Warning: Removed 252 rows containing missing values (geom_point).

# extreme filter

Glia1.ft <- subset(Glia1, subset = nFeature_RNA > 500 & nCount_RNA > 300 & nCount_RNA < 10000) 
Glia1.ft
An object of class Seurat 
33541 features across 37813 samples within 2 assays 
Active assay: RNA (33538 features, 0 variable features)
 1 other assay present: HTO
Glia1
An object of class Seurat 
33541 features across 54723 samples within 2 assays 
Active assay: RNA (33538 features, 0 variable features)
 1 other assay present: HTO
VlnPlot(Glia1.ft, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

NA
NA
NA
NA
NA

Glia2 - Radial Glia


## Filter Glia 2
VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)


VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 5000)
Warning: Removed 61 rows containing non-finite values (stat_ydensity).
Warning: Removed 61 rows containing missing values (geom_point).

VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
Warning: Removed 2435 rows containing non-finite values (stat_ydensity).
Warning: Removed 2435 rows containing missing values (geom_point).

VlnPlot(Glia2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 1000)
Warning: Removed 3194 rows containing non-finite values (stat_ydensity).
Warning: Removed 3194 rows containing missing values (geom_point).

VlnPlot(Glia2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 12000)
Warning: Removed 199 rows containing non-finite values (stat_ydensity).
Warning: Removed 199 rows containing missing values (geom_point).

# extreme filter

Glia2.ft <- subset(Glia1, subset = nFeature_RNA > 500 & nCount_RNA > 500 & nCount_RNA < 10000) 
Glia2.ft
An object of class Seurat 
33541 features across 37813 samples within 2 assays 
Active assay: RNA (33538 features, 0 variable features)
 1 other assay present: HTO
VlnPlot(Glia1.ft, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)


# there are so many suposed cells I am concerned the high read cells are actually doublets. 

Analyze each dataset - get clusters


# cluster the neurons
seu <- Neuron1.ft
seu$orig.ident <- 'Neurons1'

seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)
Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(seu), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(seu)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
When using repel, set xnudge and ynudge to 0 for optimal results
seu <- ScaleData(seu)
Centering and scaling data matrix

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seu <- RunPCA(seu)
PC_ 1 
Positive:  CDH19, MPZ, COL4A1, COL4A2, COL3A1, ZEB2, CTSC, OLFML2A, MIA, FN1 
       NRXN1, ERBB3, TGFBR2, COL14A1, COL1A2, FST, COL5A2, COL28A1, PLAT, SHC4 
       NTM, PMEPA1, KCTD12, CAVIN3, SOX10, LAMC1, BGN, IFI16, LIMCH1, GAS2L3 
Negative:  CLU, PTGDS, DLK1, SPARCL1, PTN, APOE, SAT1, TFPI2, GDF10, HPD 
       TMSB4X, C1orf61, MGST1, TRH, NRIP3, RBP4, WIF1, NUPR1, IGFBP7, LIX1 
       FGFBP1, ESM1, TPPP3, GNG11, BAALC-AS2, BAALC, LY6H, WNT2B, SFRP2, CRYAB 
PC_ 2 
Positive:  CELF4, ANK3, CACNA2D1, PCDH9, NCKAP5, CHGB, SYT1, NEUROD1, PCLO, GNB3 
       OTX2, STMN2, PTPRR, INA, OCIAD2, IMPG2, DCX, DYNC1I1, SSTR2, ZFHX4 
       BTBD8, STMN1, GRIA2, MARCH1, SLC1A2, ATP1A3, STMN4, AMER2, BEX1, FAM19A4 
Negative:  CDH19, COL3A1, MPZ, CTSC, OLFML2A, COL4A2, COL4A1, MIA, VIM, FN1 
       TGFBR2, COL1A2, ERBB3, ZEB2, S100B, SPARC, COL14A1, S100A10, IFITM3, COL5A2 
       CAVIN3, PLAT, COL28A1, SOX10, FST, PLEKHA4, GAS2L3, CXCL12, ITIH5, LGALS1 
PC_ 3 
Positive:  PCAT4, IMPG2, NEUROD1, NRXN1, CDH19, PLPPR4, TPH1, SYT1, ZEB2, MPZ 
       MIA, GSG1, STMN2, OLFML2A, SST, OLFM3, FAM19A4, CTSC, GNB3, PTPRR 
       BTBD8, COL3A1, GAS2L3, BCAT1, SOX10, COL4A2, ERBB3, CHGB, SORCS1, COL28A1 
Negative:  TPBG, SLC7A8, WLS, FSTL1, HES1, ANO10, PAPPA2, CDH2, MSX1, SLC2A1 
       ZFP36L1, PIP5K1B, NFIA, TSC22D1, SLCO1C1, SOX2, PRNP, LINC00473, SPRY1, WIF1 
       NOV, COLEC12, PLCG2, GDF10, SPATS2L, RRBP1, BMP7, PAG1, WFIKKN2, RFX4 
PC_ 4 
Positive:  EOMES, MGAT4C, ELAVL3, LHX1, RASGRP1, ADCYAP1, ELAVL4, SLC16A12, CELF4, TSHZ2 
       PTPRO, KCNK1, SCN9A, RELN, EPS8, RAB3B, SLIT1, GRID2, ASCL1, KRT222 
       ZNF385D, DCLK1, BDNF, ELAVL2, RGMB, PLCXD3, UNC5D, RALYL, PPP1R14C, DNER 
Negative:  PCAT4, TPH1, IMPG2, BCAT1, SST, FAM19A4, BTBD8, ETV3L, GSG1, PLPPR4 
       IL15, GABRG2, PDE6H, OLFM3, GNB3, CLSTN2, CRABP2, RBFOX1, AC007349.2, LINC02208 
       AIPL1, RD3, KCNH5, NCKAP5, PRKG2, AANAT, LRRC39, ANO2, ISOC1, AP000459.1 
PC_ 5 
Positive:  PTN, PTPRZ1, SPARCL1, MEGF10, ESM1, DLK1, GABBR2, ATP1A2, NRIP3, GDF10 
       NELL2, SOX2, CBLN1, APCDD1, SYTL4, SERPINI1, ARHGAP26, PTGDS, VIPR2, FTL 
       MARCKS, GNG11, TRH, IL17RD, EPHB1, RBP4, RSPO2, APOE, OGFRL1, AKR1C1 
Negative:  CYP1B1, CP, ECEL1, CXCL14, IGFBP3, WIF1, WFIKKN2, MALAT1, FHIT, EPAS1 
       SLC4A10, EMX2, PAPPA2, TRPM3, EFEMP1, BMP4, MGP, KCNJ13, ID1, EXPH5 
       KRT18, KRT8, FBLN1, MSX1, FOS, GPNMB, DCN, CDC42EP3, COL6A3, SERPINF1 
Idents(seu) <- 'orig.ident'
plot <- DimPlot(seu, reduction = "pca")


plot3 <- ElbowPlot(seu,ndims = 50)
plot3


plot2
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 13701 rows containing missing values (geom_point).

plot

NA
NA
NA

# umap

seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 43, dims = 1:25)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
08:59:48 UMAP embedding parameters a = 0.9922 b = 1.112
08:59:48 Read 1833 rows and found 25 numeric columns
08:59:48 Using Annoy for neighbor search, n_neighbors = 43
08:59:48 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:59:48 Writing NN index file to temp file /var/folders/k4/khtkczkd5tn732ftjpwgtr240000gn/T//RtmpioYRC5/file1726b7ea878e1
08:59:48 Searching Annoy index using 1 thread, search_k = 4300
08:59:48 Annoy recall = 100%
08:59:49 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 43
08:59:49 Initializing from normalized Laplacian + noise (using irlba)
08:59:49 Commencing optimization for 500 epochs, with 107418 positive edges
Using method 'umap'
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:59:53 Optimization finished
DimPlot(seu, reduction = "umap", group.by = "orig.ident")

NA
NA
NA

Make the clusters Neurons1


seu <- FindNeighbors(seu, dims = 1:25, k.param = 43)
Computing nearest neighbor graph
Computing SNN
seu <- FindClusters(seu, resolution = c(0,0.2,0.25,0.5,0.8))
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1833
Number of edges: 146997

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 1.0000
Number of communities: 1
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1833
Number of edges: 146997

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8606
Number of communities: 4
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1833
Number of edges: 146997

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8446
Number of communities: 5
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1833
Number of edges: 146997

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7787
Number of communities: 6
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1833
Number of edges: 146997

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7092
Number of communities: 8
Elapsed time: 0 seconds
seu <- FindClusters(seu, resolution = c(1.2))
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1833
Number of edges: 146997

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6403
Number of communities: 10
Elapsed time: 0 seconds
library(clustree)
Loading required package: ggraph

Attaching package: ‘ggraph’

The following object is masked from ‘package:sp’:

    geometry
clustree(seu, prefix = "RNA_snn_res.")

DimPlot(seu)

# look a lot at the clusers

VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'seurat_clusters', ncol = 1)

# these cells might be grouping by - how much MT and number of Features
# clusters 4,5,6 have more features

VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.25', ncol = 1)

# here cluster 3 has higher expression, cluster 1 and 4 have similar Features RNA
# cluster 0 has higher percent MT levels

VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.5', ncol = 1)

VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.2', ncol = 1)

# now only cluster 0 have high MT and low features, clusters 1,2,3 have simiular RNA and Counts

Find the cluster markers for Neurons1

Idents(seu) <- 'RNA_snn_res.0.2'
ClusterMarkers <- FindAllMarkers(seu)
Calculating cluster 0

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Calculating cluster 1

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Calculating cluster 2

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Calculating cluster 3

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top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.2')



#write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/ClusterMarkers_neurons1_res025.csv")


write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/ClusterMarkers_neurons1_res02.csv")
DimPlot(seu, group.by = 'RNA_snn_res.0.2', reduction = 'umap')

Get the most highly expressed genes in the total data (Neurons1)

Filters out specific genes


seu.ft <- seu[!grepl("MALAT1", rownames(seu)), ]
seu.ft <- seu.ft[!grepl("^MT-", rownames(seu.ft)), ]

Try to find doublets with doublet finder

remotes::install_github('chris-mcginnis-ucsf/DoubletFinder')
Skipping install of 'DoubletFinder' from a github remote, the SHA1 (67fb8b58) has not changed since last install.
  Use `force = TRUE` to force installation
suppressMessages(require(DoubletFinder))

Do the double cells have more genes than the singlet??


VlnPlot(seu.d, features = "nFeature_RNA", group.by = DF.name, pt.size = 0.1)

NA
NA

remove the doublets

dim(seu.d)
[1] 33538  1723
dim(seu)
[1] 33538  1833

Save the filtered, doublet removed Neurons object Re-run PCA for clustering


saveRDS(seu.d, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/NeuronsFilteredSeu28092022.RDS")

DAsubgroups data has not be re-processed Run standard workflow chunk

seu <- AIW60
seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
seu <- ScaleData(seu)
Centering and scaling data matrix

  |                                                                                                      
  |                                                                                                |   0%
  |                                                                                                      
  |================================================                                                |  50%
  |                                                                                                      
  |================================================================================================| 100%
seu <- RunPCA(seu)
PC_ 1 
Positive:  STMN2, DCX, INA, MAP2, SOX4, KIF5C, NCAM1, GAP43, SOX11, NSG2 
       ANK3, GPM6A, SYT1, TUBB2A, NRXN1, PKIA, NR2F1, STMN4, UCHL1, RTN1 
       TAGLN3, RUNX1T1, DPYSL3, NSG1, NEFM, PGM2L1, PRKAR2B, PBX1, POU2F2, ELAVL4 
Negative:  SPARC, ZFP36L1, VIM, TPBG, MDK, ANXA5, GNG5, CD9, CA2, CAST 
       ANXA2, HES1, FSTL1, NFIA, CD99, IGFBP2, TTR, IGFBP7, GSTP1, ID1 
       ID3, CYSTM1, PLTP, ZFP36L2, TRPM3, FAT1, COLEC12, B2M, SPARCL1, TMEM123 
PC_ 2 
Positive:  TTR, TPBG, IGFBP7, ANXA2, TRPM3, SLC7A8, CD9, SPINT2, CHCHD2, NFIA 
       SPARCL1, COLEC12, PPIC, PRNP, WFIKKN2, PIFO, BMP4, DMKN, LINC01088, ID1 
       MITF, CA2, ECEL1, SLC5A3, SERPINF1, CFAP126, PCP4, KRT18, SELENOP, CPVL 
Negative:  NUSAP1, TOP2A, MKI67, CENPF, PBK, CDK1, TPX2, NUF2, UBE2C, BIRC5 
       MAD2L1, CCNA2, ASPM, NCAPG, SPC25, PCLAF, CENPU, PIMREG, NDC80, KNL1 
       SMC4, KIF15, DLGAP5, SGO1, CDCA2, CDCA8, MIS18BP1, RRM2, CENPE, KIF11 
PC_ 3 
Positive:  TTYH1, PTPRZ1, NES, QKI, VIM, BOC, FGFBP3, HES5, SOX2, SLC1A3 
       RFX4, FAM181B, IGDCC3, FAM181A, EDNRB, LINC00461, PON2, RPS27L, VCAM1, CCND1 
       ARHGEF6, ZFP36L1, PLP1, JAG1, PCDH18, ITGB8, SMOC1, DLK1, TMEM38B, TFDP2 
Negative:  RTN1, STMN2, NSG2, GAP43, INA, PRKAR2B, MAPT, UCHL1, NRXN1, PKIA 
       TRPM3, IGFBP7, NSG1, C11orf88, NCAM1, SHTN1, DLGAP5, PCP4, SOBP, DCX 
       CFAP126, ANK3, TTR, NEK2, HIST1H4C, GPM6A, XPR1, CELF4, SPINT2, MITF 
PC_ 4 
Positive:  C11orf88, CAPSL, C1orf194, FAM81B, AKAP14, FAM183A, CFAP126, C9orf24, RSPH1, TCTEX1D1 
       ROPN1L, PIFO, C5orf49, CFAP52, DAW1, ARMC3, CCDC170, FAM216B, EFCAB1, MAP3K19 
       CP, SPAG6, CFAP45, AL357093.2, TEKT1, ANKRD66, MORN5, PTPRC, DYNLRB2, CFAP299 
Negative:  CNTNAP2, SYT4, ZIC1, APP, ZIC2, CBLN1, TNC, APCDD1, PTN, EPHA7 
       PAX6, SLITRK6, DSP, SPARCL1, WLS, ZFHX4, RSPO2, ATP1A2, CDK6, TXNIP 
       IL17RD, TPBG, AMER2, GDF10, ZIC4, PCDH9, WNT2B, HES1, GAP43, ITGA6 
PC_ 5 
Positive:  CALM1, TMSB4X, C11orf88, CKB, ACAT2, C1orf194, FGFBP3, C5orf49, HMGCS1, NR2F1 
       SCD, PTPRZ1, AKAP14, CAPSL, MSMO1, CFAP126, RPS2, IDI1, FAM81B, FDPS 
       FAM183A, PEG10, RSPH1, FDFT1, TUBA1B, GPM6B, ROPN1L, ARL4A, QKI, NTRK2 
Negative:  CCNB1, PLK1, UBE2C, BUB1, CDC20, ASPM, KIF20A, CENPA, CDCA8, DLGAP5 
       AURKA, KIF2C, CCNB2, NEK2, FAM83D, TTK, NUF2, PIF1, TPX2, KIF14 
       GTSE1, CDCA3, CDKN3, PIMREG, HMMR, CENPE, CDCA2, DEPDC1, CKAP2L, SGO2 
seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 123, dims = 1:30)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
13:03:03 UMAP embedding parameters a = 0.9922 b = 1.112
13:03:03 Read 15339 rows and found 30 numeric columns
13:03:03 Using Annoy for neighbor search, n_neighbors = 123
13:03:03 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:04 Writing NN index file to temp file /var/folders/k4/khtkczkd5tn732ftjpwgtr240000gn/T//RtmphW80ig/file17bc7524a9002
13:03:04 Searching Annoy index using 1 thread, search_k = 12300
13:03:14 Annoy recall = 100%
13:03:14 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 123
13:03:16 Initializing from normalized Laplacian + noise (using irlba)
13:03:17 Commencing optimization for 200 epochs, with 2087112 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:03:32 Optimization finished
DimPlot(seu, reduction = "umap")

Annotate clusters Use: Organoid data, public brain data (LaManno, Lake, Mascako)

top10 <- head(VariableFeatures(DAsubtypes.sub), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(DAsubtypes.sub)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
When using repel, set xnudge and ynudge to 0 for optimal results
plot2
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 9387 rows containing missing values (geom_point).

See how each looks on UMAP


DimPlot(seu.q, group.by = 'RNA_snn_res.1.2')

DimPlot(seu.q, group.by = 'RNA_snn_res.0.2')

DimPlot(seu.q, group.by = 'AIW60.pred')

DimPlot(seu.q, group.by = 'MBOAIW.pred')

DimPlot(seu.q, group.by = 'MBOAST23.pred')

NA
NA
FindClusters(seu.q, resolution = c(0, 0.2,0.4,0.6))
Error in FindClusters.Seurat(seu.q, resolution = c(0, 0.2, 0.4, 0.6)) : 
  Provided graph.name not present in Seurat object
NA

Redo find clusters


seu.q <- FindNeighbors(seu.q, dims = 1:25, k.param = 43)
Computing nearest neighbor graph
Computing SNN
seu.q <- FindClusters(seu.q, resolution = c(0,0.2,0.4,0.6))
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1723
Number of edges: 145289

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 1.0000
Number of communities: 1
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1723
Number of edges: 145289

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8573
Number of communities: 4
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1723
Number of edges: 145289

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7887
Number of communities: 6
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1723
Number of edges: 145289

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7394
Number of communities: 7
Elapsed time: 0 seconds
seu.q <- FindClusters(seu.q, resolution = c(1.2))
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1723
Number of edges: 145289

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6259
Number of communities: 10
Elapsed time: 0 seconds
library(clustree)
Loading required package: ggraph

Attaching package: ‘ggraph’

The following object is masked from ‘package:sp’:

    geometry
clustree(seu.q, prefix = "RNA_snn_res.")

DimPlot(seu.q)

Look at the predictions in the new clusters

Based on the 3 different predictions I can lable the cell types

0 - NPC or early neurons 1 - immature excitatory neurons 2 - NPC or early neurons 3 - RG or Oligos 4- Dopaminergic neurons - possibly early 5 - NPC or early neurons 6 - Radial GLia

I will also find markers and look at a list of neuronal markers

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
Warning in DoHeatmap(seu.q, features = top5$gene, size = 3, angle = 90,  :
  The following features were omitted as they were not found in the scale.data slot for the RNA assay: SAT1, MTRNR2L12, MT-ND2

write.csv(ClusterMarkers,"/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/Neurons1ClusterMarkers7.csv")

Explore some Gene expression levels

feature_list = c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")

DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
Warning in DoHeatmap(seu.q, features = feature_list, size = 3, angle = 90,  :
  The following features were omitted as they were not found in the scale.data slot for the RNA assay: VCAM1, CLDN11, OLIG2, PDGFRA, CD44, SLC1A3, KCNJ6, CORIN, NR4A2, LMX1B, ALDH1A1, GABRA1, GAD2, GABBR1, NCAM1, RBFOX3, NES, SOX9, DLX2, POU5F1, MKI67

DotPlot(seu.q, features = feature_list) +RotatedAxis()


PD_poulin = c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")

DoHeatmap(seu.q, features = PD_poulin, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
Warning in DoHeatmap(seu.q, features = PD_poulin, size = 3, angle = 90,  :
  The following features were omitted as they were not found in the scale.data slot for the RNA assay: VIP, CCK, GRP, SLC32A1, TACR2, ALDH1A1, SNCG, NDNF, SOX6, SLC18A2, SLC6A3

DotPlot(seu.q, features = PD_poulin)+RotatedAxis()


ealryNeur = c("DCX","NEUROD1","TBR1")
proliferation = c("PCNA","MKI67")
neuralstem = c("SOX2","NES","PAX6","MASH1")

feature_list <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
Warning in DoHeatmap(seu.q, features = feature_list, size = 3, angle = 90,  :
  The following features were omitted as they were not found in the scale.data slot for the RNA assay: MASH1, NES, MKI67, PCNA

DotPlot(seu.q, features = feature_list)+RotatedAxis()
Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
  The following requested variables were not found: MASH1

# no proliferation marker expression  PCNA or MKI67
# cluster 4 DA neurons - shows early neuron marker and low PAX 4
# cluster 3 has higher SOX2 - neuroblast marker / NPC marker

mat_neuron = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6") 
# NeuN is FOX3 - RBFOX3
# PSD95 also SP-90 or DLG4
# VGLUT2 is SLC17A6
DoHeatmap(seu.q, features = mat_neuron, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
Warning in DoHeatmap(seu.q, features = mat_neuron, size = 3, angle = 90,  :
  The following features were omitted as they were not found in the scale.data slot for the RNA assay: HOMER1, BSN, TUBB3, VAMP1, DLG45, SYP, RBFOX3

# cluster 4 also show mature neuron markers
DotPlot(seu.q, features = mat_neuron)+RotatedAxis()
Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
  The following requested variables were not found: DLG45

# excitatory neuron markers
ex = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
DoHeatmap(seu.q, features = ex, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
Warning in DoHeatmap(seu.q, features = ex, size = 3, angle = 90, group.bar.height = 0.02,  :
  The following features were omitted as they were not found in the scale.data slot for the RNA assay: CAMK2A, GRIP1, GRIN3, GRIN3A, GRIN2A, GRIN1, GRIA4

DotPlot(seu.q, features = ex)+RotatedAxis()
Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
  The following requested variables were not found: GRIN3

# inhibitory neuron markers
inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
DoHeatmap(seu.q, features = inh, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
Warning in DoHeatmap(seu.q, features = inh, size = 3, angle = 90, group.bar.height = 0.02,  :
  The following features were omitted as they were not found in the scale.data slot for the RNA assay: TRAK2, GABRA1, GABRA6, GABRB3, GABRB1, GBRR1, GABR1, GABR2, PVALB, GAT1, GAD2

DotPlot(seu.q, features = inh)+RotatedAxis()
Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
  The following requested variables were not found: GAT1, GABR2, GABR1, GBRR1

# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 

Checkout the Enricher cell type libraries from

N1.c6 <- ClusterMarkers %>% filter(cluster == 6 & avg_log2FC > 0)
genes <- N1.c6$gene

N1.c6.Er <- enrichr(genes, databases = db)
Uploading data to Enrichr... Done.
  Querying Allen_Brain_Atlas_up... Done.
  Querying Descartes_Cell_Types_and_Tissue_2021... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
plotEnrich(N1.c6.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

plotEnrich(N1.c6.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

plotEnrich(N1.c6.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

plotEnrich(N1.c6.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")


N1.Er.genes.1 <- N1.c6.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c6.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c6.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c6.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4
NA

Library of tissue cell types for up regulated genes per cluster 0 - hypothalmus, DA A13 1- neural plate, Radial Glia 2 - Neural stem 3 - stromal, astro OPC 4 - Neurons 5 - endothelial, pericyte 6 - maybe neurons maybe not

By the combined information - annotate the clusters in Neurons1

Idents(seu.q) <- 'RNA_snn_res.0.6'
cluster.ids <- c("ImmatureNeurons","Neurons","NPC","OPC-RG","DAneurons",
                 "Other","RG")
unique(seu.q$RNA_snn_res.0.6)
[1] 1 5 0 3 4 2 6
Levels: 0 1 2 3 4 5 6
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$subgroups <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'subgroups', repel = TRUE)



saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Neuron1LabledSeu30092022.RDS")

Next Repeat everything for Neurons2

seu.ft <- subset(seu, subset = nFeature_RNA > 300 & nCount_RNA > 500 & nCount_RNA < 10000) 
seu.ft
An object of class Seurat 
33541 features across 9657 samples within 2 assays 
Active assay: RNA (33538 features, 0 variable features)
 1 other assay present: HTO

Doublet finder

Remove the doublet cells


seu.d <- seu.d[, seu.d@meta.data[, DF.name]== "Singlet"]
dim(seu.d)
[1] 33524  8884
dim(seu)
[1] 33538 34830
# 9657 cells pre filter

Repeat workflow with doublet removed data and find clusters for

clustree(seu.q)

DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.2')

DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.4')

DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.6')

DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.1.2')

Label cell types using the label transfer




# SNCA and control midbrain organoids 165 days in culture
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")

# Midbrain  AIW002 120 days in culture
AIWMBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")

# Midbrain AIW002 60 days in culture

AIW60 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")


# query
#seu.q <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/NeuronsFilteredSeu28092022.RDS")


#first predict with the MBO data
Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"

# find the reference anchors
print("finding reference anchors")
[1] "finding reference anchors"
anchors <- FindTransferAnchors(reference = MBO ,query = seu.q, dims = 1:25)
Performing PCA on the provided reference using 1997 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
    Found 1228 anchors
Filtering anchors
    Retained 528 anchors
as(<ngCMatrix>, "dgCMatrix") is deprecated since Matrix 1.5-0; do as(., "dMatrix") instead
print("getting predictions")
[1] "getting predictions"
predictions <- TransferData(anchorset = anchors, refdata = MBO$cluster_labels)
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

       Epithelial Neural Precursors        Neurons-DA         Neurons-e 
                9               191               347               602 
 Oligodendrocytes               RGa              RGd1              RGd2 
             7045                 5               666                19 
Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAST23.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAST23.pred', label = TRUE)

 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")


# clusters don't break up by the predicted cell types

############ another predictions now using the AIW organoids

Idents(AIWMBO) <- "res08names"
DefaultAssay(AIWMBO) <- "RNA"

anchors <- FindTransferAnchors(reference = AIWMBO ,query = seu.q, dims = 1:25)
Performing PCA on the provided reference using 2000 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
    Found 3590 anchors
Filtering anchors
    Retained 1923 anchors
print("getting predictions")
[1] "getting predictions"
predictions <- TransferData(anchorset = anchors, refdata = AIWMBO$res08names)
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

       epithelial        Neurons_DA Neurons_early_inh               OPC 
               37               492              5556                32 
              RGa              RGd1              RGd3 
             2732                18                17 
Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAIW.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAIW.pred', label = TRUE)

 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")


# the predicted cell types make more sense from the AIW002 organoid
# now predict with the AIW002 60 days organoid

Idents(AIW60) <- "cluster.ids"
DefaultAssay(AIW60) <- "RNA"

anchors <- FindTransferAnchors(reference = AIW60, query = seu.q, dims = 1:25)
Performing PCA on the provided reference using 1999 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
    Found 3130 anchors
Filtering anchors
    Retained 1215 anchors
print("getting predictions")
[1] "getting predictions"
predictions <- TransferData(anchorset = anchors, refdata = AIW60$cluster.ids) 
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Dopaminergic Neurons early 1            Neural Epithelial 
                         950                           15 
           Neural Precursors                Radial Glia 1 
                        6332                         1572 
               Radial Glia 2 
                          15 
Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW60.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.pred', label = TRUE)

 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")


# save ojbect with predicitons
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Neurons2PredictionsSeu30092022.RDS")

See the top predictions for each cluster in Neurons2 res 06

What cell types are predicted across the 3 references

0 - Neurons early , NPC, neurons excitatory 1 - Neurons early, NPC 2 - Neurons early, NPC, neurons excitatory some DA neurons 3 - Oligo, RG, 4 - Excitatory neurons, NPC, early neurons 5 - DA neurons, early DA neurons 6 - neurons immature NPC 7 - DA neurons 8 - RG, oligo, OPC, NPC 9 - Radial Glia 10 - NPC, neurons, oligo 11 - NPC, neurons, oligo

Find cluster markers and see how those would annotate

Idents(seu.q) <- 'RNA_snn_res.0.6'
ClusterMarkers <- FindAllMarkers(seu.q, only.pos = TRUE)
Calculating cluster 0

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  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  
Calculating cluster 3

  |                                                  | 0 % ~calculating  
  |+                                                 | 2 % ~02s          
  |++                                                | 3 % ~02s          
  |+++                                               | 5 % ~02s          
  |++++                                              | 6 % ~02s          
  |++++                                              | 8 % ~02s          
  |+++++                                             | 9 % ~02s          
  |++++++                                            | 11% ~02s          
  |+++++++                                           | 12% ~02s          
  |+++++++                                           | 14% ~02s          
  |++++++++                                          | 15% ~02s          
  |+++++++++                                         | 17% ~02s          
  |++++++++++                                        | 18% ~02s          
  |++++++++++                                        | 20% ~02s          
  |+++++++++++                                       | 21% ~02s          
  |++++++++++++                                      | 23% ~02s          
  |+++++++++++++                                     | 24% ~02s          
  |+++++++++++++                                     | 26% ~02s          
  |++++++++++++++                                    | 27% ~02s          
  |+++++++++++++++                                   | 29% ~02s          
  |++++++++++++++++                                  | 30% ~02s          
  |++++++++++++++++                                  | 32% ~02s          
  |+++++++++++++++++                                 | 33% ~02s          
  |++++++++++++++++++                                | 35% ~02s          
  |+++++++++++++++++++                               | 36% ~02s          
  |+++++++++++++++++++                               | 38% ~02s          
  |++++++++++++++++++++                              | 39% ~02s          
  |+++++++++++++++++++++                             | 41% ~02s          
  |++++++++++++++++++++++                            | 42% ~02s          
  |++++++++++++++++++++++                            | 44% ~02s          
  |+++++++++++++++++++++++                           | 45% ~02s          
  |++++++++++++++++++++++++                          | 47% ~01s          
  |+++++++++++++++++++++++++                         | 48% ~01s          
  |+++++++++++++++++++++++++                         | 50% ~01s          
  |++++++++++++++++++++++++++                        | 52% ~01s          
  |+++++++++++++++++++++++++++                       | 53% ~01s          
  |++++++++++++++++++++++++++++                      | 55% ~01s          
  |+++++++++++++++++++++++++++++                     | 56% ~01s          
  |+++++++++++++++++++++++++++++                     | 58% ~01s          
  |++++++++++++++++++++++++++++++                    | 59% ~01s          
  |+++++++++++++++++++++++++++++++                   | 61% ~01s          
  |++++++++++++++++++++++++++++++++                  | 62% ~01s          
  |++++++++++++++++++++++++++++++++                  | 64% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s  
Calculating cluster 4

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~11s          
  |++                                                | 2 % ~10s          
  |++                                                | 3 % ~10s          
  |+++                                               | 4 % ~10s          
  |+++                                               | 5 % ~10s          
  |++++                                              | 7 % ~10s          
  |++++                                              | 8 % ~09s          
  |+++++                                             | 9 % ~09s          
  |+++++                                             | 10% ~09s          
  |++++++                                            | 11% ~09s          
  |+++++++                                           | 12% ~09s          
  |+++++++                                           | 13% ~09s          
  |++++++++                                          | 14% ~09s          
  |++++++++                                          | 15% ~09s          
  |+++++++++                                         | 16% ~09s          
  |+++++++++                                         | 18% ~09s          
  |++++++++++                                        | 19% ~09s          
  |++++++++++                                        | 20% ~08s          
  |+++++++++++                                       | 21% ~08s          
  |+++++++++++                                       | 22% ~08s          
  |++++++++++++                                      | 23% ~08s          
  |+++++++++++++                                     | 24% ~08s          
  |+++++++++++++                                     | 25% ~08s          
  |++++++++++++++                                    | 26% ~08s          
  |++++++++++++++                                    | 27% ~08s          
  |+++++++++++++++                                   | 29% ~08s          
  |+++++++++++++++                                   | 30% ~07s          
  |++++++++++++++++                                  | 31% ~07s          
  |++++++++++++++++                                  | 32% ~07s          
  |+++++++++++++++++                                 | 33% ~07s          
  |++++++++++++++++++                                | 34% ~07s          
  |++++++++++++++++++                                | 35% ~07s          
  |+++++++++++++++++++                               | 36% ~07s          
  |+++++++++++++++++++                               | 37% ~07s          
  |++++++++++++++++++++                              | 38% ~07s          
  |++++++++++++++++++++                              | 40% ~06s          
  |+++++++++++++++++++++                             | 41% ~06s          
  |+++++++++++++++++++++                             | 42% ~06s          
  |++++++++++++++++++++++                            | 43% ~06s          
  |++++++++++++++++++++++                            | 44% ~06s          
  |+++++++++++++++++++++++                           | 45% ~06s          
  |++++++++++++++++++++++++                          | 46% ~06s          
  |++++++++++++++++++++++++                          | 47% ~06s          
  |+++++++++++++++++++++++++                         | 48% ~06s          
  |+++++++++++++++++++++++++                         | 49% ~05s          
  |++++++++++++++++++++++++++                        | 51% ~05s          
  |++++++++++++++++++++++++++                        | 52% ~05s          
  |+++++++++++++++++++++++++++                       | 53% ~05s          
  |+++++++++++++++++++++++++++                       | 54% ~05s          
  |++++++++++++++++++++++++++++                      | 55% ~05s          
  |+++++++++++++++++++++++++++++                     | 56% ~05s          
  |+++++++++++++++++++++++++++++                     | 57% ~05s          
  |++++++++++++++++++++++++++++++                    | 58% ~04s          
  |++++++++++++++++++++++++++++++                    | 59% ~04s          
  |+++++++++++++++++++++++++++++++                   | 60% ~04s          
  |+++++++++++++++++++++++++++++++                   | 62% ~04s          
  |++++++++++++++++++++++++++++++++                  | 63% ~04s          
  |++++++++++++++++++++++++++++++++                  | 64% ~04s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~04s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~04s          
  |++++++++++++++++++++++++++++++++++                | 67% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~03s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~03s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~03s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~03s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~03s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~03s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~02s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 90% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=10s  
Calculating cluster 5

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~24s          
  |++                                                | 2 % ~24s          
  |++                                                | 3 % ~24s          
  |+++                                               | 4 % ~24s          
  |+++                                               | 5 % ~23s          
  |++++                                              | 6 % ~23s          
  |++++                                              | 7 % ~23s          
  |+++++                                             | 8 % ~23s          
  |+++++                                             | 9 % ~22s          
  |++++++                                            | 11% ~22s          
  |++++++                                            | 12% ~22s          
  |+++++++                                           | 13% ~22s          
  |+++++++                                           | 14% ~21s          
  |++++++++                                          | 15% ~21s          
  |++++++++                                          | 16% ~21s          
  |+++++++++                                         | 17% ~21s          
  |+++++++++                                         | 18% ~20s          
  |++++++++++                                        | 19% ~20s          
  |++++++++++                                        | 20% ~20s          
  |+++++++++++                                       | 21% ~20s          
  |++++++++++++                                      | 22% ~19s          
  |++++++++++++                                      | 23% ~19s          
  |+++++++++++++                                     | 24% ~19s          
  |+++++++++++++                                     | 25% ~19s          
  |++++++++++++++                                    | 26% ~18s          
  |++++++++++++++                                    | 27% ~18s          
  |+++++++++++++++                                   | 28% ~18s          
  |+++++++++++++++                                   | 29% ~18s          
  |++++++++++++++++                                  | 31% ~17s          
  |++++++++++++++++                                  | 32% ~17s          
  |+++++++++++++++++                                 | 33% ~17s          
  |+++++++++++++++++                                 | 34% ~16s          
  |++++++++++++++++++                                | 35% ~16s          
  |++++++++++++++++++                                | 36% ~16s          
  |+++++++++++++++++++                               | 37% ~16s          
  |+++++++++++++++++++                               | 38% ~15s          
  |++++++++++++++++++++                              | 39% ~15s          
  |++++++++++++++++++++                              | 40% ~15s          
  |+++++++++++++++++++++                             | 41% ~15s          
  |++++++++++++++++++++++                            | 42% ~14s          
  |++++++++++++++++++++++                            | 43% ~14s          
  |+++++++++++++++++++++++                           | 44% ~14s          
  |+++++++++++++++++++++++                           | 45% ~14s          
  |++++++++++++++++++++++++                          | 46% ~13s          
  |++++++++++++++++++++++++                          | 47% ~13s          
  |+++++++++++++++++++++++++                         | 48% ~13s          
  |+++++++++++++++++++++++++                         | 49% ~13s          
  |++++++++++++++++++++++++++                        | 51% ~12s          
  |++++++++++++++++++++++++++                        | 52% ~12s          
  |+++++++++++++++++++++++++++                       | 53% ~12s          
  |+++++++++++++++++++++++++++                       | 54% ~12s          
  |++++++++++++++++++++++++++++                      | 55% ~11s          
  |++++++++++++++++++++++++++++                      | 56% ~11s          
  |+++++++++++++++++++++++++++++                     | 57% ~11s          
  |+++++++++++++++++++++++++++++                     | 58% ~10s          
  |++++++++++++++++++++++++++++++                    | 59% ~10s          
  |++++++++++++++++++++++++++++++                    | 60% ~10s          
  |+++++++++++++++++++++++++++++++                   | 61% ~10s          
  |++++++++++++++++++++++++++++++++                  | 62% ~09s          
  |++++++++++++++++++++++++++++++++                  | 63% ~09s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~09s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~09s          
  |++++++++++++++++++++++++++++++++++                | 66% ~08s          
  |++++++++++++++++++++++++++++++++++                | 67% ~08s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~08s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~08s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~07s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~07s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~07s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~07s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~06s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~06s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~06s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~06s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~05s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=25s  
Calculating cluster 6

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~05s          
  |++                                                | 2 % ~04s          
  |++                                                | 4 % ~04s          
  |+++                                               | 5 % ~04s          
  |+++                                               | 6 % ~04s          
  |++++                                              | 7 % ~04s          
  |+++++                                             | 8 % ~04s          
  |+++++                                             | 9 % ~04s          
  |++++++                                            | 11% ~04s          
  |++++++                                            | 12% ~04s          
  |+++++++                                           | 13% ~04s          
  |++++++++                                          | 14% ~04s          
  |++++++++                                          | 15% ~04s          
  |+++++++++                                         | 16% ~03s          
  |+++++++++                                         | 18% ~03s          
  |++++++++++                                        | 19% ~03s          
  |++++++++++                                        | 20% ~03s          
  |+++++++++++                                       | 21% ~03s          
  |++++++++++++                                      | 22% ~03s          
  |++++++++++++                                      | 24% ~03s          
  |+++++++++++++                                     | 25% ~03s          
  |+++++++++++++                                     | 26% ~03s          
  |++++++++++++++                                    | 27% ~03s          
  |+++++++++++++++                                   | 28% ~03s          
  |+++++++++++++++                                   | 29% ~03s          
  |++++++++++++++++                                  | 31% ~03s          
  |++++++++++++++++                                  | 32% ~03s          
  |+++++++++++++++++                                 | 33% ~03s          
  |++++++++++++++++++                                | 34% ~03s          
  |++++++++++++++++++                                | 35% ~03s          
  |+++++++++++++++++++                               | 36% ~03s          
  |+++++++++++++++++++                               | 38% ~03s          
  |++++++++++++++++++++                              | 39% ~03s          
  |++++++++++++++++++++                              | 40% ~03s          
  |+++++++++++++++++++++                             | 41% ~02s          
  |++++++++++++++++++++++                            | 42% ~02s          
  |++++++++++++++++++++++                            | 44% ~02s          
  |+++++++++++++++++++++++                           | 45% ~02s          
  |+++++++++++++++++++++++                           | 46% ~02s          
  |++++++++++++++++++++++++                          | 47% ~02s          
  |+++++++++++++++++++++++++                         | 48% ~02s          
  |+++++++++++++++++++++++++                         | 49% ~02s          
  |++++++++++++++++++++++++++                        | 51% ~02s          
  |++++++++++++++++++++++++++                        | 52% ~02s          
  |+++++++++++++++++++++++++++                       | 53% ~02s          
  |++++++++++++++++++++++++++++                      | 54% ~02s          
  |++++++++++++++++++++++++++++                      | 55% ~02s          
  |+++++++++++++++++++++++++++++                     | 56% ~02s          
  |+++++++++++++++++++++++++++++                     | 58% ~02s          
  |++++++++++++++++++++++++++++++                    | 59% ~02s          
  |++++++++++++++++++++++++++++++                    | 60% ~02s          
  |+++++++++++++++++++++++++++++++                   | 61% ~02s          
  |++++++++++++++++++++++++++++++++                  | 62% ~02s          
  |++++++++++++++++++++++++++++++++                  | 64% ~02s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~01s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~01s          
  |++++++++++++++++++++++++++++++++++                | 67% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~01s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~01s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~01s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~01s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~01s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 88% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 94% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s  
Calculating cluster 7

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~32s          
  |++                                                | 2 % ~32s          
  |++                                                | 3 % ~32s          
  |+++                                               | 4 % ~32s          
  |+++                                               | 5 % ~31s          
  |++++                                              | 6 % ~31s          
  |++++                                              | 7 % ~31s          
  |+++++                                             | 8 % ~30s          
  |+++++                                             | 9 % ~30s          
  |++++++                                            | 10% ~30s          
  |++++++                                            | 11% ~29s          
  |+++++++                                           | 12% ~29s          
  |+++++++                                           | 14% ~29s          
  |++++++++                                          | 15% ~28s          
  |++++++++                                          | 16% ~28s          
  |+++++++++                                         | 17% ~27s          
  |+++++++++                                         | 18% ~27s          
  |++++++++++                                        | 19% ~27s          
  |++++++++++                                        | 20% ~26s          
  |+++++++++++                                       | 21% ~26s          
  |+++++++++++                                       | 22% ~26s          
  |++++++++++++                                      | 23% ~25s          
  |++++++++++++                                      | 24% ~25s          
  |+++++++++++++                                     | 25% ~25s          
  |++++++++++++++                                    | 26% ~24s          
  |++++++++++++++                                    | 27% ~24s          
  |+++++++++++++++                                   | 28% ~24s          
  |+++++++++++++++                                   | 29% ~24s          
  |++++++++++++++++                                  | 30% ~23s          
  |++++++++++++++++                                  | 31% ~23s          
  |+++++++++++++++++                                 | 32% ~22s          
  |+++++++++++++++++                                 | 33% ~22s          
  |++++++++++++++++++                                | 34% ~22s          
  |++++++++++++++++++                                | 35% ~21s          
  |+++++++++++++++++++                               | 36% ~21s          
  |+++++++++++++++++++                               | 38% ~21s          
  |++++++++++++++++++++                              | 39% ~20s          
  |++++++++++++++++++++                              | 40% ~20s          
  |+++++++++++++++++++++                             | 41% ~20s          
  |+++++++++++++++++++++                             | 42% ~19s          
  |++++++++++++++++++++++                            | 43% ~19s          
  |++++++++++++++++++++++                            | 44% ~18s          
  |+++++++++++++++++++++++                           | 45% ~18s          
  |+++++++++++++++++++++++                           | 46% ~18s          
  |++++++++++++++++++++++++                          | 47% ~17s          
  |++++++++++++++++++++++++                          | 48% ~17s          
  |+++++++++++++++++++++++++                         | 49% ~17s          
  |+++++++++++++++++++++++++                         | 50% ~16s          
  |++++++++++++++++++++++++++                        | 51% ~16s          
  |+++++++++++++++++++++++++++                       | 52% ~16s          
  |+++++++++++++++++++++++++++                       | 53% ~15s          
  |++++++++++++++++++++++++++++                      | 54% ~15s          
  |++++++++++++++++++++++++++++                      | 55% ~15s          
  |+++++++++++++++++++++++++++++                     | 56% ~14s          
  |+++++++++++++++++++++++++++++                     | 57% ~14s          
  |++++++++++++++++++++++++++++++                    | 58% ~14s          
  |++++++++++++++++++++++++++++++                    | 59% ~13s          
  |+++++++++++++++++++++++++++++++                   | 60% ~13s          
  |+++++++++++++++++++++++++++++++                   | 61% ~13s          
  |++++++++++++++++++++++++++++++++                  | 62% ~12s          
  |++++++++++++++++++++++++++++++++                  | 64% ~12s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~11s          
  |+++++++++++++++++++++++++++++++++                 | 66% ~11s          
  |++++++++++++++++++++++++++++++++++                | 67% ~11s          
  |++++++++++++++++++++++++++++++++++                | 68% ~10s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~10s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~10s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~09s          
  |++++++++++++++++++++++++++++++++++++              | 72% ~09s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~09s          
  |+++++++++++++++++++++++++++++++++++++             | 74% ~08s          
  |++++++++++++++++++++++++++++++++++++++            | 75% ~08s          
  |+++++++++++++++++++++++++++++++++++++++           | 76% ~08s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~07s          
  |++++++++++++++++++++++++++++++++++++++++          | 78% ~07s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++         | 80% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++        | 82% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 84% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 86% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~02s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=32s  
Calculating cluster 8

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~10s          
  |++                                                | 2 % ~09s          
  |++                                                | 3 % ~09s          
  |+++                                               | 4 % ~09s          
  |+++                                               | 6 % ~09s          
  |++++                                              | 7 % ~09s          
  |++++                                              | 8 % ~09s          
  |+++++                                             | 9 % ~09s          
  |++++++                                            | 10% ~09s          
  |++++++                                            | 11% ~08s          
  |+++++++                                           | 12% ~08s          
  |+++++++                                           | 13% ~08s          
  |++++++++                                          | 15% ~08s          
  |++++++++                                          | 16% ~08s          
  |+++++++++                                         | 17% ~08s          
  |+++++++++                                         | 18% ~08s          
  |++++++++++                                        | 19% ~08s          
  |+++++++++++                                       | 20% ~08s          
  |+++++++++++                                       | 21% ~08s          
  |++++++++++++                                      | 22% ~07s          
  |++++++++++++                                      | 24% ~07s          
  |+++++++++++++                                     | 25% ~07s          
  |+++++++++++++                                     | 26% ~07s          
  |++++++++++++++                                    | 27% ~07s          
  |+++++++++++++++                                   | 28% ~07s          
  |+++++++++++++++                                   | 29% ~07s          
  |++++++++++++++++                                  | 30% ~07s          
  |++++++++++++++++                                  | 31% ~07s          
  |+++++++++++++++++                                 | 33% ~07s          
  |+++++++++++++++++                                 | 34% ~06s          
  |++++++++++++++++++                                | 35% ~06s          
  |++++++++++++++++++                                | 36% ~06s          
  |+++++++++++++++++++                               | 37% ~06s          
  |++++++++++++++++++++                              | 38% ~06s          
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Calculating cluster 9

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Calculating cluster 10

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Calculating cluster 11

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top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
Warning in DoHeatmap(seu.q, features = top5$gene, size = 3, angle = 90,  :
  The following features were omitted as they were not found in the scale.data slot for the RNA assay: MTRNR2L8, MTRNR2L12, SLC1A3, TPT1, RPL37

write.csv(ClusterMarkers,"/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/Neurons1ClusterMarkers11.csv")

Cluster 0 has fewer markers. 2 and 5 have similar up reg markers 3 and 4 also overlap

Look at the cluster markers in cell type libraries for Neurons 2

N1.c6 <- ClusterMarkers %>% filter(cluster == 11 & avg_log2FC > 0)
genes <- N1.c6$gene

N1.c6.Er <- enrichr(genes, databases = db)
Uploading data to Enrichr... Done.
  Querying Allen_Brain_Atlas_up... Done.
  Querying Descartes_Cell_Types_and_Tissue_2021... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
plotEnrich(N1.c6.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

plotEnrich(N1.c6.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

plotEnrich(N1.c6.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

plotEnrich(N1.c6.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")


N1.Er.genes.1 <- N1.c6.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c6.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c6.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c6.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4
NA

cluster 0 - astrocyte, radial glia, microglia, striatum - very few genes in these terms cluster 1 - adrenal, thalmus, endothelial, astrocytes, neurons clusters 2 - neural plate, stratum, neurons, NPC, stem, astro,neuroendocrine cluster 3 - brain molecular layer, endothelial, astrocyte embryonic, cluster 4 - DG, striatum CA3, GABAergic neurons cluster 5 - DG, neurons, Glutamatergic neurons cluster 6 - neurons, astrocyte, microglia, GABAneurons cluster 7 - neurons, glut and gaba cluster 8 - astrocyte, endothelial, pericyte cluster 9 - epithelial, embryonic astrocytes, GABA neurons cluster 10 - epithelial cluster 11 - endothelial, immune cells T cells

Expression of markers genes in Neurons2

features <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
              "ITGB5","IL6ST")
DoHeatmap(seu.q, features = features, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
Warning in DoHeatmap(seu.q, features = features, size = 3, angle = 90, group.bar.height = 0.02,  :
  The following features were omitted as they were not found in the scale.data slot for the RNA assay: IL6ST, ITGB5, LIFR, HOPX, FAM107A, ADGRE1

DotPlot(seu.q, features = features, group.by = 'RNA_snn_res.0.6')+RotatedAxis()

Label the Neuron2 FACS population

Idents(seu.q) <- 'RNA_snn_res.0.6'
cluster.ids <- c("Neurons1","Neurons2","Neurons3",
                 "Other","DAneurons1","DAneurons2","Neurons4",
                 "DAneurons3","RG","Neurons2","Epithelial","Endothelial")
unique(seu.q$RNA_snn_res.0.6)
 [1] 0  2  1  6  8  3  5  7  4  10 9  11
Levels: 0 1 2 3 4 5 6 7 8 9 10 11
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$number.groups <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'number.groups', repel = TRUE)

Proportions of cell types

Find markers in pairs to go back and classify the subgroups. Will need to return to this for Neurons1 FACS

Neurons 5 and Neurons2 had similar markers and were merged Subset again


top5 <- neuron.sub.markers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(neuron.sub, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)

DotPlot(neuron.sub, features = top5$gene) + RotatedAxis()
Warning: Scaling data with a low number of groups may produce misleading results

Use subgrouping and find cluster markers to look at neuronal subtypes.


top5 <- neuron.sub.markers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(neuron.sub, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)

DotPlot(neuron.sub, features = top5$gene) + RotatedAxis()
Warning: Scaling data with a low number of groups may produce misleading results

Name the Neurons2 FACS population with the Neuron subtype latter.


Idents(seu.q) <- 'RNA_snn_res.0.6'
cluster.ids <- c("Neurons-MSX1","Neurons-LY6H","Neurons-ASCL1",
                 "Other","DAneurons-CYP1B1","DAneurons-ASCL1","Neurons-GK5",
                 "DAneurons-NEUROD1","RG","Neurons-LY6H","Epithelial","Endothelial")

unique(seu.q$RNA_snn_res.0.6)
 [1] 0  2  1  6  8  3  5  7  4  10 9  11
Levels: 0 1 2 3 4 5 6 7 8 9 10 11
names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$cellsubgroups <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'cellsubgroups', repel = TRUE)

NA
NA

# save the Neurons2 with labels

saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Neurons2LabelsSeu30092022.RDS")

FACS population Glia1 (should be astrocytes)

seu.ft
An object of class Seurat 
33541 features across 47295 samples within 2 assays 
Active assay: RNA (33538 features, 0 variable features)
 1 other assay present: HTO

saveRDS(seu.ft, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1AstroSeu01102022.RDS")

Doublet finder


suppressMessages(require(DoubletFinder))

# filtering out MALAT1 and mitochondrial genes

seu.ft <- seu.ft[!grepl("MALAT1", rownames(seu.ft)), ]
seu.ft <- seu.ft[!grepl("^MT-", rownames(seu.ft)), ]

# like in the tutorial I'm following MALAT1 is the top most expressed gene.  The top genes are a lot of MT and Ribosomal genes

seu.ft[["percent.rb"]] <- PercentageFeatureSet(seu.ft, pattern = "^RP")

# down sample there are too many cells to run doublet finder
seu.sub <- subset(seu.ft, downsample = 20000)

seu.d = NormalizeData(seu.sub)
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
seu.d = FindVariableFeatures(seu.d, verbose = F)
seu.d = ScaleData(seu.d, vars.to.regress = c("nFeature_RNA", "percent.mt"),
    verbose = F)
seu.d = RunPCA(seu.d, verbose = F, npcs = 15)
seu.d = RunUMAP(seu.d, dims = 1:10, verbose = F)

nExp <- round(ncol(seu.d) * 0.15)  # expect more doublets because there is a lot more cells
seu.d <- doubletFinder_v3(seu.d, pN = 0.25, pK = 0.09, nExp = nExp, PCs = 1:10)
Loading required package: fields
Loading required package: spam
Spam version 2.9-1 (2022-08-07) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction 
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.

Attaching package: ‘spam’

The following object is masked from ‘package:Matrix’:

    det

The following objects are masked from ‘package:base’:

    backsolve, forwardsolve

Loading required package: viridis
Loading required package: viridisLite

Try help(fields) to get started.
Loading required package: KernSmooth
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
[1] "Creating 6667 artificial doublets..."
[1] "Creating Seurat object..."
[1] "Normalizing Seurat object..."
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] "Finding variable genes..."
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] "Scaling data..."
Centering and scaling data matrix

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  |====================================                                     |  50%
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  |=========================================================================| 100%
[1] "Running PCA..."
[1] "Calculating PC distance matrix..."
[1] "Computing pANN..."
[1] "Classifying doublets.."
# the memory limit is reached here - I could run on compute canada
# For now I'll downsample
# this works

# name of the DF prediction can change, so extract the correct column name.
DF.name = colnames(seu.d@meta.data)[grepl("DF.classification", colnames(seu.d@meta.data))]


cowplot::plot_grid(ncol = 2, DimPlot(seu.d, group.by = "orig.ident") + NoAxes(),
    DimPlot(seu.d, group.by = DF.name) + NoAxes())


VlnPlot(seu.d, features = "nFeature_RNA", group.by = DF.name, pt.size = 0.1)

Remove the doublet cells

dim(seu.d)
[1] 33524 17000
dim(seu.sub)
[1] 33524 20000

Repeat workflow with doublet removed data and find clusters for

seu <- NormalizeData(seu.d, normalization.method = "LogNormalize", scale.factor = 10000)
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
seu <- ScaleData(seu)
Centering and scaling data matrix

  |                                                                               
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  |=========================================================================| 100%
seu <- RunPCA(seu)
PC_ 1 
Positive:  TMSB10, COL3A1, LGALS1, IGFBP4, PPIB, DCN, COL1A1, CALD1, S100A6, COL1A2 
       TIMP1, COL6A3, S100A4, NDUFA4L2, LUM, IFITM3, VIM, S100A11, VCAN, COL6A1 
       SELENOM, RPL10, RPL18A, APOE, RACK1, MDK, LGALS3, COL6A2, SERPINF1, RPS23 
Negative:  PMCH, CLU, AF131216.1, YJEFN3, AC068389.1, AC005014.2, MMP21, PTPRZ1, PLEK2, AC096667.1 
       S100A9, PITX1, AC010524.1, INA, DDI1, GPHA2, AC240565.1, IMPG2, AC243829.1, AC098934.4 
       ADAMTS20, DMRT3, AC022784.1, AC034198.2, MFNG, STPG3-AS1, PTPRH, AC092634.5, IGKV1-6, AC010422.2 
PC_ 2 
Positive:  COL3A1, COL1A2, COL1A1, DCN, VCAN, LUM, COL6A3, IGFBP4, S100A4, COL6A1 
       NDUFA4L2, APOD, COL4A1, OGN, COL6A2, APOE, S100A6, MFAP4, COL4A2, EDNRA 
       PRRX1, TMSB10, COL5A2, TNFAIP6, THY1, FN1, COL5A1, IFITM3, CYTL1, CFH 
Negative:  S100B, IGFBP2, TPBG, CRYAB, LY6H, HPD, KRT18, GSTP1, KRT8, METRN 
       SOX2, ARL4C, ITM2C, PTPRZ1, SERTM1, HES1, DPCD, CA2, PLTP, ABHD14A 
       PAPPA2, MGST1, CBLN1, PSAT1, BEX1, ESM1, WFIKKN2, HIST1H4C, EFEMP1, LIX1 
PC_ 3 
Positive:  PTGDS, SERPINF1, ECEL1, PRSS56, APOC1, BST2, ID1, FABP5, CITED4, RBP1 
       ASS1, TMEM176A, CDKN1C, TMEM176B, KDR, DCN, EMX2, ASCL1, APOE, SOD3 
       CCNG2, RPL39, DES, EMX2OS, WIF1, ADRA2C, RPL10, RPL7A, RPL32, RAMP1 
Negative:  SCG2, PTPRZ1, ESM1, COL1A1, COL1A2, TFPI2, GABBR2, VCAN, FN1, OGN 
       ABCA8, GNG11, LUM, NTRK2, COL3A1, S100A10, SOX2, RFX4, COL4A1, COL6A1 
       PTN, ITPR2, CBLN1, APOD, SPARCL1, NR2F1, LY6H, TNFAIP6, SLC7A11, ANXA1 
PC_ 4 
Positive:  CXCL14, IGFBP2, PCP4, OGN, CA2, CP, WFIKKN2, IGFBP7, IGF1, PLAC9 
       SPINT2, FHIT, GPNMB, KRT18, EFEMP1, DKK3, COL1A2, KRT8, FOLR1, VCAN 
       DMKN, CRABP1, TPBG, SERF2, ENPP2, COL1A1, PRRX1, CRABP2, TNFAIP6, KCNJ13 
Negative:  APOE, PTN, DLK1, SCG2, APOC1, DCN, FABP5, GDF10, PTPRZ1, CLU 
       ESM1, KDR, PRSS56, PEG10, TFPI2, SPARCL1, RDH10, RFX4, GNG11, SLC7A2 
       IFITM1, BST2, GABBR2, ABCA8, COL23A1, ITM2C, CITED4, KCNQ1OT1, CCNG2, SYNDIG1 
PC_ 5 
Positive:  KCNQ1OT1, NEAT1, DSP, CYP1B1, CP, KHDRBS2, SLC5A3, SLC4A10, AKAP12, LINC00473 
       PTP4A1, HMGCS1, PAPPA2, IGFBP3, WFIKKN2, AC092683.1, HTR2C, IGFBP5, WIF1, SLCO1C1 
       UACA, TRPM3, LAMB1, KCNJ13, SPARCL1, MDM2, RAB3IP, KDR, CA2, SV2C 
Negative:  RPS12, RPL10, RPL39, RPS23, RPL32, FTL, RPS28, RPL26, RPL12, RPS3 
       RPL7A, RPL8, RPS4X, RPL18A, GSTP1, IFI27L2, VIM, FTH1, S100A10, RACK1 
       IFI27, GAPDH, SERF2, GNG11, TXN, LY6H, HIST1H4C, LGALS1, TFPI2, RPS4Y1 
seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 43, dims = 1:30)
12:20:48 UMAP embedding parameters a = 0.9922 b = 1.112
12:20:48 Read 20000 rows and found 30 numeric columns
12:20:48 Using Annoy for neighbor search, n_neighbors = 43
12:20:48 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:20:49 Writing NN index file to temp file /var/folders/k4/khtkczkd5tn732ftjpwgtr240000gn/T//RtmpOCo8CT/file74b220cfcf06
12:20:49 Searching Annoy index using 1 thread, search_k = 4300
12:20:55 Annoy recall = 100%
12:20:56 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 43
12:20:57 Initializing from normalized Laplacian + noise (using irlba)
12:20:58 Commencing optimization for 200 epochs, with 1288572 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
12:21:12 Optimization finished
DimPlot(seu, reduction = "umap")


seu.q <- FindNeighbors(seu, dims = 1:25, k.param = 43)
Computing nearest neighbor graph
Computing SNN
seu.q <- FindClusters(seu.q, resolution = c(0,0.2,0.4,0.6))
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 20000
Number of edges: 760620

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 1.0000
Number of communities: 1
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 20000
Number of edges: 760620

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8636
Number of communities: 6
Elapsed time: 2 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 20000
Number of edges: 760620

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8123
Number of communities: 10
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 20000
Number of edges: 760620

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7734
Number of communities: 11
Elapsed time: 4 seconds
seu.q <- FindClusters(seu.q, resolution = c(0,0.05,0.1,0.8))
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 20000
Number of edges: 760620

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 1.0000
Number of communities: 1
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 20000
Number of edges: 760620

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9522
Number of communities: 3
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 20000
Number of edges: 760620

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9166
Number of communities: 4
Elapsed time: 3 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 20000
Number of edges: 760620

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.7376
Number of communities: 13
Elapsed time: 4 seconds
library(clustree)
Loading required package: ggraph

Attaching package: ‘ggraph’

The following object is masked from ‘package:sp’:

    geometry
clustree(seu.q)

DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.05')

DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.1')

DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.2')

DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.4')

DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.6')

DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.8')

NA
NA
NA
NA

Look at some expression markers in a feature plot

# genes reported up in Astrocytes
FeaturePlot(seu.q, features = c("GFAP","S100B","AQP4","SLC1A3","GJA1",
                                "APOE","TEAD1","GSTA4","SOX9",
                                "VIM","HMG20A","ALDH1L1"))

# almost no GFAP expression and lots of S100B everywhere

Predict cell types



# SNCA and control midbrain organoids 165 days in culture
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")

# Midbrain  AIW002 120 days in culture
AIWMBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")

# Midbrain AIW002 60 days in culture

AIW60 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")


#first predict with the MBO data
Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"

# find the reference anchors
print("finding reference anchors")
[1] "finding reference anchors"
anchors <- FindTransferAnchors(reference = MBO ,query = seu.q, dims = 1:25)
Performing PCA on the provided reference using 1997 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
    Found 814 anchors
Filtering anchors
    Retained 219 anchors
as(<ngCMatrix>, "dgCMatrix") is deprecated since Matrix 1.5-0; do as(., "dMatrix") instead
print("getting predictions")
[1] "getting predictions"
predictions <- TransferData(anchorset = anchors, refdata = MBO$cluster_labels)
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

      Epithelial        Neurons-e Oligodendrocytes             RGd1 
             587              451            11247             7689 
            RGd2 
              26 
Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAST23.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAST23.pred', label = TRUE)

 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.8, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")


# clusters don't break up by the predicted cell types

############ another predictions now using the AIW organoids

Idents(AIWMBO) <- "res08names"
DefaultAssay(AIWMBO) <- "RNA"

anchors <- FindTransferAnchors(reference = AIWMBO ,query = seu.q, dims = 1:25)
Performing PCA on the provided reference using 2000 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
    Found 3639 anchors
Filtering anchors
    Retained 1713 anchors
print("getting predictions")
[1] "getting predictions"
predictions <- TransferData(anchorset = anchors, refdata = AIWMBO$res08names)
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

       epithelial        Neurons_DA Neurons_early_inh             oligo 
               60                 1              5622               110 
              OPC               RGa              RGd1              RGd3 
              241             13950                 3                13 
Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAIW.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAIW.pred', label = TRUE)

 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.8, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")


# the predicted cell types make more sense from the AIW002 organoid
# now predict with the AIW002 60 days organoid

Idents(AIW60) <- "cluster.ids"
DefaultAssay(AIW60) <- "RNA"

anchors <- FindTransferAnchors(reference = AIW60, query = seu.q, dims = 1:25)
Performing PCA on the provided reference using 1999 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
    Found 2402 anchors
Filtering anchors
    Retained 645 anchors
print("getting predictions")
[1] "getting predictions"
predictions <- TransferData(anchorset = anchors, refdata = AIW60$cluster.ids) 
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

                  Astrocytes Dopaminergic Neurons early 1 
                           1                           31 
           Neural Epithelial            Neural Precursors 
                          12                         7145 
               Radial Glia 1                Radial Glia 2 
                       12806                            5 
Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW60.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.pred', label = TRUE)

 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.8, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")


# save ojbect with predicitons
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1PredictionsSeu01102022.RDS")

Look at the reference objects


# triplication
DimPlot(MBO)

# AIW 60 days
DimPlot(AIW60)

# AIW 120 days
DimPlot(AIWMBO)

NA
NA

Look more at the predictions


# AIW002 120 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 

top.pred.celltype.AIW120 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw120 <- top.pred.celltype.AIW120[order(top.pred.celltype.AIW120$Var1,-top.pred.celltype.AIW120$Freq),]
row.names(df.top.aiw120) <- NULL
df.top.aiw120$I <- row.names(df.top.aiw120)

# AIW002 60 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 

top.pred.celltype.AIW60 <-as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw60 <- top.pred.celltype.AIW60[order(top.pred.celltype.AIW60$Var1,-top.pred.celltype.AIW60$Freq),]
row.names(df.top.aiw60) <- NULL
df.top.aiw60$I <- row.names(df.top.aiw60)


# AST23 165 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 

top.pred.celltype.AST23 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.AST23 <- top.pred.celltype.AST23[order(top.pred.celltype.AST23$Var1,-top.pred.celltype.AST23$Freq),]
row.names(df.top.AST23) <- NULL
df.top.AST23$I <- row.names(df.top.AST23)

pred.table <- merge(df.top.AST23, df.top.aiw60, by = 'I', all = TRUE)
pred.table <- merge(pred.table, df.top.aiw120, by = 'I')
pred.table
NA

These predictions are not good. There are several astrocyte markers by expression levels. Everything is predicted as Radial glia or oligo dendrocytes

Try to predict with the astrocyte Kamath data

Look at cluster markers

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)
Warning in DoHeatmap(seu.q, features = top5$gene, size = 3, angle = 90,  :
  The following features were omitted as they were not found in the scale.data slot for the RNA assay: LRRC75A, SNHG25

Check cell type markers with EnrichR

N1.c0 <- ClusterMarkers %>% filter(cluster == 5 & avg_log2FC > 0)
genes <- N1.c0$gene

N1.c0.Er <- enrichr(genes, databases = db)
Uploading data to Enrichr... Done.
  Querying Allen_Brain_Atlas_up... Done.
  Querying Descartes_Cell_Types_and_Tissue_2021... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
plotEnrich(N1.c0.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

plotEnrich(N1.c0.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

plotEnrich(N1.c0.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

plotEnrich(N1.c0.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")


N1.Er.genes.1 <- N1.c0.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c0.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c0.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3


N1.Er.genes.4 <- N1.c0.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4



# cluster 0 - Cell type marker library - Brain astrocyte top hit and embryonic astrocytes
# gene list in term: brain astrocyte    EFEMP1;NFIA;LIX1;PSAP;KIF21A;S100B;CRYAB;DKK3
# embryo astrocytes SOX2;BEX1;HMGCS1;PTPRZ1;LIX1;S100B;DKK3;ITM2C

# cluster 1 - stem cell pericyte (brain), stelate, astrocyte

# cluster 2 - hypothalmus, endothelial cells, macrophage
# endothelial CSTB;PRELID1;MT1X;CRIP2;RHOC;TMEM141;MT2A;RPS28;CCDC85B;EIF3I;RBP1;ID1;C4ORF3;ID3;PCBD1;MSX1;PPIC

# cluster 3 - smooth muscle cells

# cluster 4 - NK cells, fibroblasts
# NK cells ITGB1;RAB5C;GSTP1;PDCD5;EEF1B2;TGOLN2;SDCBP;MT2A;LDHA;SNRPD2;YWHAQ;ZNF326;TMSB10;CCDC50
# fibroblasts   COL3A1;CALD1;COL6A3

# cluster 5 - endothelial cells, NK cells, CD8+

# cluster 6 - stromal cells eurythroblasts, none-neuronal, oligo

# reran and now there are only 5 clusters
# repeat checking

Cluster 0 - astrocytes Cluster 1 - pericyte astrocyte (weak still) Cluster 2 - endothelial Cluster 3 - smooth muscle Cluster 4 - NK/fibroblast Cluster 5 - endothelial Cluster 6 - non- neuronal


VlnPlot(seu.q, features = c("CD44","ITGB1","S100B"), group.by = 'orig.ident')

VlnPlot(seu.ft, features = c("CD44","ITGB1","S100B"), group.by = 'orig.ident')

Check expression of known markers

features <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
              "ITGB5","IL6ST")
DoHeatmap(seu.q, features = features, size=3, angle =90, group.bar.height = 0.02)
Warning in DoHeatmap(seu.q, features = features, size = 3, angle = 90, group.bar.height = 0.02) :
  The following features were omitted as they were not found in the scale.data slot for the RNA assay: IL6ST, ITGB5, LIFR, ADGRE1, AIF1

DotPlot(seu.q, features = features)+RotatedAxis()

No TH expression

clusters 5 VIM highest, S100 B Cluster 4 Cluster 3 has some cells with high OTX2, NES indicates NPC/Precursors Cluster 2 has some SOX2 and PAX6 indicates NPC, VAMP2 indicating neurons, S100B highest and most - indicates astrocytes, also MBP indicates oligos Cluster 1 Cluster 0

Lable the clusters


Idents(seu.q) <- 'RNA_snn_res.0.2'

#seu.q <- BuildClusterTree(seu.q, reorder = TRUE, reorder.numeric = TRUE)
unique(seu.q$RNA_snn_res.0.2)
[1] 0 1 4 3 2 5
Levels: 0 1 2 3 4 5
cluster.ids <- c("Astrocytes1","Astrocytes2","Precursors","RG1","RG2","Endothelial")

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$subgroups <- Idents(seu.q)

#DimPlot(seu.q, group.by = 'RNA_snn_res.0.2', label = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'subgroups', repel = TRUE)


# something weird is going on in the order

#saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1LabledSeu301102022.RDS")

Compare the Astrocyte groups and get some markers for sub groups

npc.markers <- npc.markers %>% filter(avg_log2FC > 0)
dim(npc.markers)
[1] 292   5

Add subtype gene ids

Proportions of cells types

Quick check the Glial2


seu.ft <- subset(seu, subset = nFeature_RNA > 250 & nCount_RNA > 250 & nCount_RNA < 10000) 
seu.ft
An object of class Seurat 
33541 features across 7553 samples within 2 assays 
Active assay: RNA (33538 features, 0 variable features)
 1 other assay present: HTO
VlnPlot(seu.ft, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 2000)
Warning: Removed 1310 rows containing non-finite values (stat_ydensity).
Warning: Removed 1310 rows containing missing values (geom_point).


VlnPlot(seu.ft.glia, features = c("CD44","S100B","ITGB1"))

VlnPlot(seu.ft, features = c("CD44","S100B","ITGB1"), group.by = 'orig.ident')

Levels seem similar in Glia1 and Glia2

Remove doublets and start to process Glia 2


suppressMessages(require(DoubletFinder))

# filtering out MALAT1 and mitochondrial genes

seu.ft <- seu.ft[!grepl("MALAT1", rownames(seu.ft)), ]
seu.ft <- seu.ft[!grepl("^MT-", rownames(seu.ft)), ]

# like in the tutorial I'm following MALAT1 is the top most expressed gene.  The top genes are a lot of MT and Ribosomal genes

seu.ft[["percent.rb"]] <- PercentageFeatureSet(seu.ft, pattern = "^RP")

seu.d = NormalizeData(seu.ft)
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
seu.d = FindVariableFeatures(seu.d, verbose = F)
seu.d = ScaleData(seu.d, vars.to.regress = c("nFeature_RNA", "percent.mt"),
    verbose = F)
seu.d = RunPCA(seu.d, verbose = F, npcs = 15)
seu.d = RunUMAP(seu.d, dims = 1:10, verbose = F)

nExp <- round(ncol(seu.d) * 0.08)  # expect more doublets because there is a lot more cells
seu.d <- doubletFinder_v3(seu.d, pN = 0.25, pK = 0.09, nExp = nExp, PCs = 1:10)
[1] "Creating 2518 artificial doublets..."
[1] "Creating Seurat object..."
[1] "Normalizing Seurat object..."
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] "Finding variable genes..."
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
[1] "Scaling data..."
Centering and scaling data matrix

  |                                                                                 
  |                                                                           |   0%
  |                                                                                 
  |======================================                                     |  50%
  |                                                                                 
  |===========================================================================| 100%
[1] "Running PCA..."
[1] "Calculating PC distance matrix..."
[1] "Computing pANN..."
[1] "Classifying doublets.."
# the memory limit is reached here - I could run on compute canada
# For now I'll downsample
# this works

# name of the DF prediction can change, so extract the correct column name.
DF.name = colnames(seu.d@meta.data)[grepl("DF.classification", colnames(seu.d@meta.data))]


cowplot::plot_grid(ncol = 2, DimPlot(seu.d, group.by = "orig.ident") + NoAxes(),
    DimPlot(seu.d, group.by = DF.name) + NoAxes())


VlnPlot(seu.d, features = "nFeature_RNA", group.by = DF.name, pt.size = 0.1)


seu.d <- seu.d[, seu.d@meta.data[, DF.name]== "Singlet"]
dim(seu.d)
[1] 33524  6949
dim(seu.ft)
[1] 33524  7553

Cluster

Predict cell types


#first predict with the MBO data
Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"

# find the reference anchors
print("finding reference anchors")
[1] "finding reference anchors"
anchors <- FindTransferAnchors(reference = MBO ,query = seu.q, dims = 1:25)
Performing PCA on the provided reference using 1997 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
    Found 1209 anchors
Filtering anchors
    Retained 559 anchors
print("getting predictions")
[1] "getting predictions"
predictions <- TransferData(anchorset = anchors, refdata = MBO$cluster_labels)
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
seu.q <- AddMetaData(seu.q, metadata = predictions)

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAST23.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAST23.pred', label = TRUE)


# see how accurate the predictions are
seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "None")

Idents(seu.q) <- 'predicted.id'
seu.q$MBOAST23.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'predicted.id', label = TRUE)

table(seu.q$MBOAST23.pred)

             RGd1  Oligodendrocytes         Neurons-e Neural Precursors        Neurons-DA 
             1176              4567               976               117               105 
       Epithelial 
                8 
table(seu.q$MBOAST23.thresh)

             RGd1              None  Oligodendrocytes         Neurons-e        Neurons-DA 
              254              5203              1446                33                10 
Neural Precursors 
                3 
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.4, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")


# clusters don't break up by the predicted cell types

############ another predictions now using the AIW organoids

Idents(AIWMBO) <- "res08names"
DefaultAssay(AIWMBO) <- "RNA"

anchors <- FindTransferAnchors(reference = AIWMBO ,query = seu.q, dims = 1:25)
Performing PCA on the provided reference using 2000 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
    Found 3816 anchors
Filtering anchors
    Retained 2248 anchors
print("getting predictions")
[1] "getting predictions"
predictions <- TransferData(anchorset = anchors, refdata = AIWMBO$res08names)
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

       epithelial        Neurons_DA Neurons_early_inh             oligo               OPC 
               31               217              4407                76               119 
              RGa              RGd3 
             2074                25 
Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW120.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW120.pred', label = TRUE)

 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.4, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)
# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")


# see how accurate the predictions are
seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "None")

Idents(seu.q) <- 'predicted.id'
seu.q$AIW120.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW120.thresh', label = TRUE)

table(seu.q$AIW120.pred)

              RGa Neurons_early_inh        Neurons_DA             oligo               OPC 
             2074              4407               217                76               119 
             RGd3        epithelial 
               25                31 
table(seu.q$AIW120.thresh)

              RGa Neurons_early_inh              None        Neurons_DA               OPC 
             1052              2735              2861               177               110 
             RGd3        epithelial 
               10                 4 
# the predicted cell types make more sense from the AIW002 organoid
# now predict with the AIW002 60 days organoid

Idents(AIW60) <- "cluster.ids"
DefaultAssay(AIW60) <- "RNA"

anchors <- FindTransferAnchors(reference = AIW60, query = seu.q, dims = 1:25)
Performing PCA on the provided reference using 1999 features as input.
Projecting cell embeddings
Finding neighborhoods
Finding anchors
    Found 3345 anchors
Filtering anchors
    Retained 1582 anchors
print("getting predictions")
[1] "getting predictions"
predictions <- TransferData(anchorset = anchors, refdata = AIW60$cluster.ids) 
Finding integration vectors
Finding integration vector weights
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Predicting cell labels
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Dopaminergic Neurons early 1            Neural Epithelial            Neural Precursors 
                         218                           25                         4973 
               Radial Glia 1 
                        1733 
Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW60.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.pred', label = TRUE)

 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.4, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")


# see how accurate the predictions are
seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "None")

Idents(seu.q) <- 'predicted.id'
seu.q$AIW60.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.thresh', label = TRUE)

table(seu.q$AIW60.pred)

               Radial Glia 1            Neural Precursors Dopaminergic Neurons early 1 
                        1733                         4973                          218 
           Neural Epithelial 
                          25 
table(seu.q$AIW60.thresh)

               Radial Glia 1            Neural Precursors                         None 
                         972                         3138                         2740 
Dopaminergic Neurons early 1 
                          99 
# save with predictions so far
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")

See how many cells are predicted as astrocytes with the threshold

DAsubtypes <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/DAsubgroups_processed.Rds")

Idents(astro.ref) <- "Cell_Subtype"
DefaultAssay(astro.ref) <- "RNA"

# find the reference anchors
print("finding reference anchors")
[1] "finding reference anchors"
anchors <- FindTransferAnchors(reference = DAsubtypes ,query = seu.q, dims = 1:20)
Error: vector memory exhausted (limit reached?)

Do these get labelled as DA neurons too???

seu.q <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")
Warning message:
R graphics engine version 15 is not supported by this version of RStudio. The Plots tab will be disabled until a newer version of RStudio is installed. 

seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "none")

Idents(seu.q) <- 'predicted.id'
seu.q$da.pred.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'da.pred.thresh', label = TRUE)
table(seu.q$da.pred.thresh)

      none CALB1_RBP4 
      6213        736 

Compare predictions - make a predictions table


# AIW002 120 days predictions - take the thresholded options
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$AIW120.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 

top.pred.celltype.AIW120 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw120 <- top.pred.celltype.AIW120[order(top.pred.celltype.AIW120$Var1,-top.pred.celltype.AIW120$Freq),]
row.names(df.top.aiw120) <- NULL
df.top.aiw120$I <- row.names(df.top.aiw120)

# AIW002 60 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$AIW60.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation
*** recursive gc invocation

top.pred.celltype.AIW60 <-as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw60 <- top.pred.celltype.AIW60[order(top.pred.celltype.AIW60$Var1,-top.pred.celltype.AIW60$Freq),]
row.names(df.top.aiw60) <- NULL
df.top.aiw60$I <- row.names(df.top.aiw60)


# AST23 165 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$MBOAST23.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 

top.pred.celltype.AST23 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.AST23 <- top.pred.celltype.AST23[order(top.pred.celltype.AST23$Var1,-top.pred.celltype.AST23$Freq),]
row.names(df.top.AST23) <- NULL
df.top.AST23$I <- row.names(df.top.AST23)

# add the threshold Astro predictions 
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$astro.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 

top.pred.celltype.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.celltype.astro[order(top.pred.celltype.astro$Var1,-top.pred.celltype.astro$Freq),]
row.names(df.top.astro) <- NULL
df.top.astro$I <- row.names(df.top.astro)

# add the neurons predictions 
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$da.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 

top.pred.celltype.da <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.da <- top.pred.celltype.da[order(top.pred.celltype.da$Var1,-top.pred.celltype.da$Freq),]
row.names(df.top.da) <- NULL
df.top.da$I <- row.names(df.top.da)

pred.table <- merge(df.top.AST23, df.top.aiw60, by = 'I', all = TRUE)
pred.table <- merge(pred.table, df.top.aiw120, by = 'I')
pred.table <- merge(pred.table, df.top.astro, by = 'I')
Warning in merge.data.frame(pred.table, df.top.astro, by = "I") :
  column names ‘Var1.x’, ‘Var2.x’, ‘Freq.x’, ‘Var1.y’, ‘Var2.y’, ‘Freq.y’ are duplicated in the result
pred.table <- merge(pred.table, df.top.da, by = 'I')
Warning in merge.data.frame(pred.table, df.top.da, by = "I") :
  column names ‘Var1.x’, ‘Var2.x’, ‘Freq.x’, ‘Var1.y’, ‘Var2.y’, ‘Freq.y’ are duplicated in the result
pred.table
NA
NA

Predicted cluster annotations 0 Unknown/ NPC 1 RG 2 astro 3 RG 4 neurons 5 RG

Look at gene lists with known markers

rg <- c("VIM","NES","PAX6","HES1","EAAT1","NCAD1","SOX2","FABP7")
DoHeatmap(seu.q, features = rg, size=3, angle =90, group.bar.height = 0.02)
Warning in DoHeatmap(seu.q, features = rg, size = 3, angle = 90, group.bar.height = 0.02) :
  The following features were omitted as they were not found in the scale.data slot for the RNA assay: NCAD1, EAAT1, PAX6, NES

DotPlot(seu.q, features = rg)+RotatedAxis()
Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
  The following requested variables were not found: EAAT1, NCAD1

Marker expression predictions Cluster 0 - unknown Cluster 1 - RG Cluster 2 - unknown Cluster 3 - RG cluster 4 - immature neurons Cluster 5 - RG, opc

Check the levels of RNA in each cluster

VlnPlot(seu.q, features = "nFeature_RNA")

Cluster 0 and 2 have fewer sequences than other groups and thus no markers Possibly remove these is they don’t come up with some markers

Find cluster markers

Idents(seu.q) <- 'RNA_snn_res.0.1'
ClusterMarkers <- FindAllMarkers(seu.q, only.pos = TRUE)
Calculating cluster 0

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~10s          
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Calculating cluster 1

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Calculating cluster 2

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Calculating cluster 3

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top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)

Markers of 2 are matching with 5 possibly merge these together Cluster 0 markers don’t look up regulated but the list is long

Look at the libraries


N1.c0 <- ClusterMarkers %>% filter(cluster == 0 & avg_log2FC > 0)
genes <- N1.c0$gene

N1.c0.Er <- enrichr(genes, databases = db)
Uploading data to Enrichr... Done.
  Querying Descartes_Cell_Types_and_Tissue_2021... Done.
  Querying CellMarker_Augmented_2021... Done.
  Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
plotEnrich(N1.c0.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

plotEnrich(N1.c0.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

plotEnrich(N1.c0.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")



N1.Er.genes.1 <- N1.c0.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c0.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c0.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

Add some cell type annotations


Idents(seu.q) <- 'RNA_snn_res.0.2'

cluster.ids <- c("Glia1","RG1","Glia2","RG2","NeuronsImmature","RG3")

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$subgroups <- Idents(seu.q)

#DimPlot(seu.q, group.by = 'RNA_snn_res.0.2', label = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'subgroups', repel = TRUE)

NA
NA
NA
# save file
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")

Main cell groups


Idents(seu.q) <- 'RNA_snn_res.0.2'

cluster.ids <- c("RG","RG","RG","RG","NeuronsImmature","RG")

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Cell_Types <- Idents(seu.q)

#DimPlot(seu.q, group.by = 'RNA_snn_res.0.2', label = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Cell_Types', repel = TRUE)


saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")

Proportions of cell types

I’ll calculate the proportions for each cell type and make a table or plot in the comparison workbook.

---
title: "R Notebook"
output: html_notebook
---
Single cell seq after sorting for PhenoID

sample1 = Neurons1
sample2 = Neurons2
sample3 = Glia1 - Astrocytes (CD44+)
sample4 = Glia2 - Radial Glia (CD44-)

In HPC I have run steps of scrnabox (custom pipeline in progress)
1. Cell Ranger for feature seq
2. Create Seurat Objects 
3. Apply minimum filtering and calculate percent mitochondria.

I have technical 3 replicates with hashtag labels at this point I haven't yet demultiplex the hashtags. The data here will be treated as one sample.  I sorted three separate samples and pooled them together. 


```{r}
# set up the environment

library(Seurat)
library(dplyr)
library(Matrix)
library(ggplot2)

#rm(list = ls())


```


Read in the seurat objects made in compute canada

```{r}

# this seems to never load I'll use step 3 output that has some filtering 
# nFeature_RNA > 180 and percent.mt < 25

pathway <- "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/"

Neurons1 <- readRDS(paste(pathway,"seu1.rds",sep = ""))
Neurons2 <- readRDS(paste(pathway,"seu2.rds",sep = ""))
Glia1 <- readRDS(paste(pathway,"seu3.rds",sep = ""))
Glia2 <- readRDS(paste(pathway,"seu4.rds",sep = ""))

Neurons1
Neurons2
Glia1
Glia2


```


Have a look at the objects that already have some filtering


See the violin plots 

```{r}

VlnPlot(Neurons1, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(Neurons1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)
VlnPlot(Neurons1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)



# filter more cells

Neuron1.ft <- subset(Neurons1, subset = nFeature_RNA > 250 & nCount_RNA > 250 & nCount_RNA < 10000) 
Neuron1.ft

# 33541 features across 1833 samples


```

Neurons 2 - CD56++

```{r}

VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)
VlnPlot(Neurons2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
VlnPlot(Neurons2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)



# filter more cells

Neuron2.ft <- subset(Neurons2, subset = nFeature_RNA > 500 & nCount_RNA > 500 & nCount_RNA < 10000) 
Neuron2.ft



```

Glia1 - Astrocyte data

```{r}

VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 5000)
VlnPlot(Glia1, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
VlnPlot(Glia1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 1000)
VlnPlot(Glia1, pt.size = 0.10, features = c("nCount_RNA"), y.max = 12000)


# extreme filter

Glia1.ft <- subset(Glia1, subset = nFeature_RNA > 500 & nCount_RNA > 300 & nCount_RNA < 10000) 
Glia1.ft
Glia1

VlnPlot(Glia1.ft, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)





```


Glia2 - Radial Glia

```{r}

## Filter Glia 2
VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 5000)
VlnPlot(Glia2, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
VlnPlot(Glia2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 1000)
VlnPlot(Glia2, pt.size = 0.10, features = c("nCount_RNA"), y.max = 12000)


# extreme filter

Glia2.ft <- subset(Glia1, subset = nFeature_RNA > 500 & nCount_RNA > 500 & nCount_RNA < 10000) 
Glia2.ft


VlnPlot(Glia1.ft, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

# there are so many suposed cells I am concerned the high read cells are actually doublets. 



```


Analyze each dataset - get clusters 

```{r}

# cluster the neurons
seu <- Neuron1.ft
seu$orig.ident <- 'Neurons1'

seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(seu), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(seu)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)



seu <- ScaleData(seu)
seu <- RunPCA(seu)
Idents(seu) <- 'orig.ident'
plot <- DimPlot(seu, reduction = "pca")


plot3 <- ElbowPlot(seu,ndims = 50)
plot3

plot2
plot



```

```{r}

# umap

seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 43, dims = 1:25)
DimPlot(seu, reduction = "umap", group.by = "orig.ident")



```

Make the clusters Neurons1

```{r}

seu <- FindNeighbors(seu, dims = 1:25, k.param = 43)
seu <- FindClusters(seu, resolution = c(0,0.2,0.25,0.5,0.8))
seu <- FindClusters(seu, resolution = c(1.2))

library(clustree)
clustree(seu, prefix = "RNA_snn_res.")
DimPlot(seu)

```

```{r}
# look a lot at the clusers

VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'seurat_clusters', ncol = 1)
# these cells might be grouping by - how much MT and number of Features
# clusters 4,5,6 have more features

VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.25', ncol = 1)
# here cluster 3 has higher expression, cluster 1 and 4 have similar Features RNA
# cluster 0 has higher percent MT levels

VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.5', ncol = 1)
VlnPlot(seu, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = 'RNA_snn_res.0.2', ncol = 1)
# now only cluster 0 have high MT and low features, clusters 1,2,3 have simiular RNA and Counts

```

Find the cluster markers for Neurons1

```{r}
Idents(seu) <- 'RNA_snn_res.0.2'
ClusterMarkers <- FindAllMarkers(seu)

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.2')


#write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/ClusterMarkers_neurons1_res025.csv")


write.csv(ClusterMarkers, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/ClusterMarkers_neurons1_res02.csv")

```

```{r}
DimPlot(seu, group.by = 'RNA_snn_res.0.2', reduction = 'umap')

```


Get the most highly expressed genes in the total data (Neurons1)


```{r}

par(mar = c(4, 8, 2, 1))
C <- seu@assays$RNA@counts
C <- Matrix::t(Matrix::t(C)/Matrix::colSums(C)) * 100
most_expressed <- order(apply(C, 1, median), decreasing = T)[25:1]
boxplot(as.matrix(t(C[most_expressed, ])), cex = 0.1, las = 1, xlab = "% total count per cell",
    col = (scales::hue_pal())(25)[1:25], horizontal = TRUE)

# like in the tutorial I'm following MALAT1 is the top most expressed gene.  The top genes are a lot of MT and Ribosomal genes

seu[["percent.rb"]] <- PercentageFeatureSet(seu, pattern = "^RP")

VlnPlot(seu, features = "percent.rb", group.by = "RNA_snn_res.0.2")



```

Filters out specific genes

```{r}

seu.ft <- seu[!grepl("MALAT1", rownames(seu)), ]
seu.ft <- seu.ft[!grepl("^MT-", rownames(seu.ft)), ]

# this filtered object might cluster differently
# for now I'm going to move on to doublet detection


```



Try to find doublets with doublet finder

```{r}
remotes::install_github('chris-mcginnis-ucsf/DoubletFinder')
suppressMessages(require(DoubletFinder))


```

```{r}

seu.d = FindVariableFeatures(seu, verbose = F)
seu.d = ScaleData(seu.d, vars.to.regress = c("nFeature_RNA", "percent.mt"),
    verbose = F)
seu.d = RunPCA(seu.d, verbose = F, npcs = 20)
seu.d = RunUMAP(seu.d, dims = 1:10, verbose = F)

nExp <- round(ncol(seu.d) * 0.06)  # expect 6% doublets
seu.d <- doubletFinder_v3(seu.d, pN = 0.25, pK = 0.09, nExp = nExp, PCs = 1:10)


# name of the DF prediction can change, so extract the correct column name.
DF.name = colnames(seu.d@meta.data)[grepl("DF.classification", colnames(seu.d@meta.data))]



cowplot::plot_grid(ncol = 2, DimPlot(seu.d, group.by = "orig.ident") + NoAxes(),
    DimPlot(seu.d, group.by = DF.name) + NoAxes())


```

Do the double cells have more genes than the singlet??

```{r}

VlnPlot(seu.d, features = "nFeature_RNA", group.by = DF.name, pt.size = 0.1)


```

# remove the doublets

```{r}

seu.d <- seu.d[, seu.d@meta.data[, DF.name]== "Singlet"]
dim(seu.d)
dim(seu)

# removed about 100 cells




```


Save the filtered, doublet removed Neurons object 
Re-run PCA for clustering 

```{r}

saveRDS(seu.d, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/NeuronsFilteredSeu28092022.RDS")

```


DAsubgroups data has not be re-processed
Run standard workflow chunk

```{r}

#seu <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/PD_da.Rds")
#seu <- AIW60
seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
seu <- ScaleData(seu)
seu <- RunPCA(seu)
seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 159, dims = 1:30)
DimPlot(seu, reduction = "umap")

#saveRDS(seu, "/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/DAsubgroups_processed.Rds")

#note my AIW 60 days data also didn't have the PCA saved 
# ran code chunck with n.neighbors = 123 
# saveRDS(seu, "/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")


```


Annotate clusters
Use: Organoid data, public brain data (LaManno, Lake, Mascako)


```{r}

# this is some reference data

# SNCA and control midbrain organoids 165 days in culture
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")

# Midbrain  AIW002 120 days in culture
AIWMBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")

# Midbrain AIW002 60 days in culture

AIW60 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")

# DA neuron subtypes from postmortem brain Kamath et al 2022
DAsubtypes <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/DAsubgroups_processed.Rds")


# query
seu.q <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/NeuronsFilteredSeu28092022.RDS")


#first predict with the MBO data
Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"

# find the reference anchors
print("finding reference anchors")
anchors <- FindTransferAnchors(reference = MBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = MBO$cluster_labels)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAST23.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAST23.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# clusters don't break up by the predicted cell types

############ another predictions now using the AIW organoids

Idents(AIWMBO) <- "res08names"
DefaultAssay(AIWMBO) <- "RNA"

anchors <- FindTransferAnchors(reference = AIWMBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIWMBO$res08names)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAIW.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAIW.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# the predicted cell types make more sense from the AIW002 organoid
# now predict with the AIW002 60 days organoid

Idents(AIW60) <- "cluster.ids"
DefaultAssay(AIW60) <- "RNA"

anchors <- FindTransferAnchors(reference = AIW60, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIW60$cluster.ids) 
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW60.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# save ojbect with predicitons
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/NeuronsFilteredSeu28092022.RDS")



```

```{r}
# compare the three predictions
#AST23 vs AIW60
t.lables <- as.data.frame(table(seu.q$MBOAST23.pred, seu.q$AIW60.pred))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()

#AST23 vs AIW120
t.lables <- as.data.frame(table(seu.q$MBOAST23.pred, seu.q$MBOAIW.pred))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()


# AIW60 vs AIW120
t.lables <- as.data.frame(table(seu.q$AIW60.pred, seu.q$MBOAIW.pred))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis()


```

See how each looks on UMAP

```{r}

DimPlot(seu.q, group.by = 'RNA_snn_res.1.2')
DimPlot(seu.q, group.by = 'RNA_snn_res.0.2')
DimPlot(seu.q, group.by = 'AIW60.pred')
DimPlot(seu.q, group.by = 'MBOAIW.pred')
DimPlot(seu.q, group.by = 'MBOAST23.pred')


```

```{r}

# redo clusters
seu.q <- NormalizeData(seu.q, normalization.method = "LogNormalize", scale.factor = 10000)
seu.q <- FindVariableFeatures(seu.q, selection.method = "vst", nfeatures = 2000)
seu.q <- ScaleData(seu.q)
seu.q <- RunPCA(seu.q)
seu.q <- RunUMAP(seu.q, reduction = "pca", n.neighbors = 25, dims = 1:30, min.dist = 0.25, spread = 2)
DimPlot(seu.q, reduction = "umap", group.by = 'MBOAIW.pred')





```


Redo find clusters

```{r}

seu.q <- FindNeighbors(seu.q, dims = 1:25, k.param = 43)
seu.q <- FindClusters(seu.q, resolution = c(0,0.2,0.4,0.6))
seu.q <- FindClusters(seu.q, resolution = c(1.2))

library(clustree)
clustree(seu.q, prefix = "RNA_snn_res.")
DimPlot(seu.q)
```

Look at the predictions in the new clusters

```{r}
# AIW002 160 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.6, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW120 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(1, Freq))

# AIW002 160 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.6, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW60 <-as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(1, Freq))

# AST23 65 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.6, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AST23 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(1, Freq))


pred.table <- merge(top.pred.celltype.AIW120,top.pred.celltype.AIW60, by = 'Var1')
pred.table <- merge(pred.table, top.pred.celltype.AST23, by = 'Var1')
pred.table

```

Based on the 3 different predictions I can lable the cell types

0 - NPC or early neurons
1 - immature excitatory neurons
2 - NPC or early neurons
3 - RG or Oligos
4- Dopaminergic neurons - possibly early
5 - NPC or early neurons
6 - Radial GLia

I will also find markers and look at a list of neuronal markers

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.6'
ClusterMarkers <- FindAllMarkers(seu.q, only.pos = TRUE)

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')

write.csv(ClusterMarkers,"/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/Neurons1ClusterMarkers7.csv")


```

Explore some Gene expression levels

```{r}
feature_list = c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")

DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = feature_list) +RotatedAxis()

PD_poulin = c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")

DoHeatmap(seu.q, features = PD_poulin, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = PD_poulin)+RotatedAxis()

ealryNeur = c("DCX","NEUROD1","TBR1")
proliferation = c("PCNA","MKI67")
neuralstem = c("SOX2","NES","PAX6","MASH1")

feature_list <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = feature_list)+RotatedAxis()
# no proliferation marker expression  PCNA or MKI67
# cluster 4 DA neurons - shows early neuron marker and low PAX 4
# cluster 3 has higher SOX2 - neuroblast marker / NPC marker

mat_neuron = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6") 
# NeuN is FOX3 - RBFOX3
# PSD95 also SP-90 or DLG4
# VGLUT2 is SLC17A6
DoHeatmap(seu.q, features = mat_neuron, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
# cluster 4 also show mature neuron markers
DotPlot(seu.q, features = mat_neuron)+RotatedAxis()
# excitatory neuron markers
ex = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
DoHeatmap(seu.q, features = ex, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = ex)+RotatedAxis()
# inhibitory neuron markers
inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
DoHeatmap(seu.q, features = inh, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = inh)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 



```


Checkout the Enricher cell type libraries from 

```{r}
# test markers for the 7 clusters in Neurons1 

library(devtools)
install_github("wjawaid/enrichR")
library(enrichR)


setEnrichrSite("Enrichr") # Human genes
# list of all the databases

dbs <- listEnrichrDbs()
dbs
# libaries with cell types

db <- c('Allen_Brain_Atlas_up','Descartes_Cell_Types_and_Tissue_2021',
        'CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')

# enrichr(genes, databases = NULL)

N1.c0 <- ClusterMarkers %>% filter(cluster == 0 & avg_log2FC > 0)
genes <- N1.c0$gene

N1.c0.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c0.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c0.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c0.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c0.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

# cluster 0 could be hypothalmus, DA neurons A13

N1.c1 <- ClusterMarkers %>% filter(cluster == 1 & avg_log2FC > 0)
genes <- N1.c1$gene

N1.c1.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c1.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c1.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c1.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c1.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c1.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c1.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c1.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c1.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 1; olfactory bulb, neural plate, maybe Radial Glia, 
N1.c2 <- ClusterMarkers %>% filter(cluster == 2 & avg_log2FC > 0)
genes <- N1.c2$gene

N1.c2.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c2.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c2.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c2.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c2.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c2.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c2.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c2.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c2.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 2 some brain nucleus, neural stem

N1.c3 <- ClusterMarkers %>% filter(cluster == 3 & avg_log2FC > 0)
genes <- N1.c3$gene

N1.c3.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c3.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c3.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c3.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c3.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c3.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c3.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c3.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c3.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 3 stromal cell of thymus, embryonic astrocytes, OPC, NK cells, monocytes

N1.c4 <- ClusterMarkers %>% filter(cluster == 4 & avg_log2FC > 0)
genes <- N1.c4$gene

N1.c4.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c4.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c4.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c4.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c4.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c4.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c4.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c4.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c4.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# Dentate gyrus - different cortical layers, neurons, neurons, NPC, neurons GABA,GLUT

N1.c5 <- ClusterMarkers %>% filter(cluster == 5 & avg_log2FC > 0)
genes <- N1.c5$gene

N1.c5.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c5.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c5.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c5.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c5.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c5.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c5.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c5.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c5.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 5 hippocampus, endothelial cells, pericytes

N1.c6 <- ClusterMarkers %>% filter(cluster == 6 & avg_log2FC > 0)
genes <- N1.c6$gene

N1.c6.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c6.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c6.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c6.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c6.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c6.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 6 brain cortex, shwann cell, endothelial, pericyte, GABA


```

Library of tissue cell types for up regulated genes per cluster
0 - hypothalmus, DA A13
1- neural plate, Radial Glia
2 - Neural stem
3 - stromal, astro OPC
4 - Neurons
5 - endothelial, pericyte
6 - maybe neurons maybe not


By the combined information - annotate the clusters in Neurons1

```{r}
#Based on the 3 different predictions I can lable the cell types

#0 - NPC or early neurons
#1 - immature excitatory neurons
#2 - NPC or early neurons
#3 - RG or Oligos
#4- Dopaminergic neurons - possibly early
#5 - NPC or early neurons
#6 - Radial Glia

Idents(seu.q) <- 'RNA_snn_res.0.6'
cluster.ids <- c("ImmatureNeurons","Neurons","NPC","OPC-RG","DAneurons",
                 "Other","RG")
unique(seu.q$RNA_snn_res.0.6)

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$subgroups <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'subgroups', repel = TRUE)


saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Neuron1LabledSeu30092022.RDS")

```


### Next Repeat everything for Neurons2

```{r}
# explore filtering
seu <- Neurons2
seu
VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 2000)
VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 350)
VlnPlot(seu, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)

# filter more cells

seu.ft <- subset(seu, subset = nFeature_RNA > 300 & nCount_RNA > 500 & nCount_RNA < 10000) 
seu.ft

# 17604 samples with 250 nFeature_RNA
# 9657 with  nFeature 300 and nCOunt 500


```


Doublet finder 

```{r}
suppressMessages(require(DoubletFinder))

# filtering out MALAT1 and mitochondrial genes

seu.ft <- seu.ft[!grepl("MALAT1", rownames(seu)), ]
seu.ft <- seu.ft[!grepl("^MT-", rownames(seu.ft)), ]

# like in the tutorial I'm following MALAT1 is the top most expressed gene.  The top genes are a lot of MT and Ribosomal genes

seu.ft[["percent.rb"]] <- PercentageFeatureSet(seu.ft, pattern = "^RP")

seu.d = NormalizeData(seu.ft)
seu.d = FindVariableFeatures(seu.d, verbose = F)
seu.d = ScaleData(seu.d, vars.to.regress = c("nFeature_RNA", "percent.mt"),
    verbose = F)
seu.d = RunPCA(seu.d, verbose = F, npcs = 30)
seu.d = RunUMAP(seu.d, dims = 1:10, verbose = F)

nExp <- round(ncol(seu.d) * 0.08)  # expect more doublets because there is a lot more cells
seu.d <- doubletFinder_v3(seu.d, pN = 0.25, pK = 0.09, nExp = nExp, PCs = 1:10)


# name of the DF prediction can change, so extract the correct column name.
DF.name = colnames(seu.d@meta.data)[grepl("DF.classification", colnames(seu.d@meta.data))]


cowplot::plot_grid(ncol = 2, DimPlot(seu.d, group.by = "orig.ident") + NoAxes(),
    DimPlot(seu.d, group.by = DF.name) + NoAxes())

VlnPlot(seu.d, features = "nFeature_RNA", group.by = DF.name, pt.size = 0.1)

```


Remove the doublet cells

```{r}

seu.d <- seu.d[, seu.d@meta.data[, DF.name]== "Singlet"]
dim(seu.d)
dim(seu)
# 9657 cells pre filter
# 8884 cells after filtering
# note the percent doubles expected is close to the percent detected

```


Repeat workflow with doublet removed data and find clusters for 

```{r}


seu <- NormalizeData(seu.d, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
seu <- ScaleData(seu)
seu <- RunPCA(seu)
seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 43, dims = 1:30)
DimPlot(seu, reduction = "umap")

seu.q <- FindNeighbors(seu, dims = 1:25, k.param = 43)
seu.q <- FindClusters(seu.q, resolution = c(0,0.2,0.4,0.6))

library(clustree)
clustree(seu.q)

DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.2')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.4')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.6')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.1.2')

```

Label cell types using the label transfer

```{r}



# SNCA and control midbrain organoids 165 days in culture
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")

# Midbrain  AIW002 120 days in culture
AIWMBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")

# Midbrain AIW002 60 days in culture

AIW60 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")


# query
#seu.q <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/NeuronsFilteredSeu28092022.RDS")


#first predict with the MBO data
Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"

# find the reference anchors
print("finding reference anchors")
anchors <- FindTransferAnchors(reference = MBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = MBO$cluster_labels)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAST23.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAST23.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# clusters don't break up by the predicted cell types

############ another predictions now using the AIW organoids

Idents(AIWMBO) <- "res08names"
DefaultAssay(AIWMBO) <- "RNA"

anchors <- FindTransferAnchors(reference = AIWMBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIWMBO$res08names)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAIW.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAIW.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# the predicted cell types make more sense from the AIW002 organoid
# now predict with the AIW002 60 days organoid

Idents(AIW60) <- "cluster.ids"
DefaultAssay(AIW60) <- "RNA"

anchors <- FindTransferAnchors(reference = AIW60, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIW60$cluster.ids) 
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW60.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id))
pr.t.lables <- as.data.frame(prop.table(table(seu.q$RNA_snn_res.0.2, seu.q$predicted.id)))
t.lables$Freq <- as.double(t.lables$Freq)


# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# save ojbect with predicitons
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Neurons2PredictionsSeu30092022.RDS")



```

See the top predictions for each cluster in Neurons2 res 06 


```{r}

# AIW002 120 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.6, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW120 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw120 <- top.pred.celltype.AIW120[order(top.pred.celltype.AIW120$Var1,-top.pred.celltype.AIW120$Freq),]
row.names(df.top.aiw120) <- NULL
df.top.aiw120$I <- row.names(df.top.aiw120)

# AIW002 60 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.6, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW60 <-as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw60 <- top.pred.celltype.AIW60[order(top.pred.celltype.AIW60$Var1,-top.pred.celltype.AIW60$Freq),]
row.names(df.top.aiw60) <- NULL
df.top.aiw60$I <- row.names(df.top.aiw60)


# AST23 165 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.6, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AST23 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.AST23 <- top.pred.celltype.AST23[order(top.pred.celltype.AST23$Var1,-top.pred.celltype.AST23$Freq),]
row.names(df.top.AST23) <- NULL
df.top.AST23$I <- row.names(df.top.AST23)

pred.table <- merge(df.top.AST23, df.top.aiw60, by = 'I', all = TRUE)
pred.table <- merge(pred.table, df.top.aiw120, by = 'I')
pred.table

```

What cell types are predicted across the 3 references

0 - Neurons early , NPC, neurons excitatory
1 - Neurons early, NPC
2 - Neurons early, NPC, neurons excitatory some DA neurons
3 - Oligo, RG, 
4 - Excitatory neurons, NPC, early neurons
5 - DA neurons, early DA neurons
6 - neurons immature NPC
7 - DA neurons
8 - RG, oligo, OPC, NPC
9 - Radial Glia
10 - NPC, neurons, oligo
11 - NPC, neurons, oligo


Find cluster markers and see how those would annotate

```{r}
Idents(seu.q) <- 'RNA_snn_res.0.6'
ClusterMarkers <- FindAllMarkers(seu.q, only.pos = TRUE)

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')

write.csv(ClusterMarkers,"/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/Neurons1ClusterMarkers11.csv")

```
Cluster 0 has fewer markers. 
2 and 5 have similar up reg markers
3 and 4 also overlap



Look at the cluster markers in cell type libraries for Neurons 2

```{r}
library(enrichR)
db <- c('Allen_Brain_Atlas_up','Descartes_Cell_Types_and_Tissue_2021',
        'CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')

# enrichr(genes, databases = NULL)
# cluster 0

N1.c0 <- ClusterMarkers %>% filter(cluster == 0 & avg_log2FC > 0)
genes <- N1.c0$gene

N1.c0.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c0.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c0.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c0.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c0.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

# cluster 1

N1.c1 <- ClusterMarkers %>% filter(cluster == 1 & avg_log2FC > 0)
genes <- N1.c1$gene

N1.c1.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c1.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c1.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c1.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c1.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c1.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c1.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c1.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c1.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 1; olfactory bulb, neural plate, maybe Radial Glia, 
N1.c2 <- ClusterMarkers %>% filter(cluster == 2 & avg_log2FC > 0)
genes <- N1.c2$gene

N1.c2.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c2.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c2.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c2.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c2.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c2.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c2.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c2.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c2.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 3

N1.c3 <- ClusterMarkers %>% filter(cluster == 3 & avg_log2FC > 0)
genes <- N1.c3$gene

N1.c3.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c3.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c3.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c3.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c3.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c3.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c3.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c3.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c3.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 4

N1.c4 <- ClusterMarkers %>% filter(cluster == 4 & avg_log2FC > 0)
genes <- N1.c4$gene

N1.c4.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c4.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c4.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c4.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c4.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c4.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c4.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c4.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c4.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 5

N1.c5 <- ClusterMarkers %>% filter(cluster == 5 & avg_log2FC > 0)
genes <- N1.c5$gene

N1.c5.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c5.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c5.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c5.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c5.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c5.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c5.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c5.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c5.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4

# cluster 6

N1.c6 <- ClusterMarkers %>% filter(cluster == 6 & avg_log2FC > 0)
genes <- N1.c6$gene

N1.c6.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c6.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c6.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c6.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c6.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c6.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4


# other clusters - change the cluster number
N1.c6 <- ClusterMarkers %>% filter(cluster == 11 & avg_log2FC > 0)
genes <- N1.c6$gene

N1.c6.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c6.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c6.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c6.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c6.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c6.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3

N1.Er.genes.4 <- N1.c6.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4


```

cluster 0 - astrocyte, radial glia, microglia, striatum - very few genes in these terms
cluster 1 - adrenal, thalmus, endothelial, astrocytes, neurons
clusters 2 - neural plate, stratum, neurons, NPC, stem, astro,neuroendocrine
cluster 3 - brain molecular layer, endothelial, astrocyte embryonic, 
cluster 4 - DG, striatum CA3, GABAergic neurons
cluster 5 - DG, neurons, Glutamatergic neurons
cluster 6 - neurons, astrocyte, microglia, GABAneurons
cluster 7 - neurons, glut and gaba
cluster 8 - astrocyte, endothelial, pericyte
cluster 9 - epithelial, embryonic astrocytes, GABA neurons
cluster 10 - epithelial
cluster 11 - endothelial, immune cells T cells


Expression of markers genes in Neurons2 

```{r}

feature_list = c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")

DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = feature_list) +RotatedAxis()

PD_poulin = c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")

DoHeatmap(seu.q, features = PD_poulin, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = PD_poulin)+RotatedAxis()

ealryNeur = c("DCX","NEUROD1","TBR1")
proliferation = c("PCNA","MKI67")
neuralstem = c("SOX2","NES","PAX6","MASH1")

feature_list <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = feature_list)+RotatedAxis()
# no proliferation marker expression  PCNA or MKI67
# cluster 4 DA neurons - shows early neuron marker and low PAX 4
# cluster 3 has higher SOX2 - neuroblast marker / NPC marker

mat_neuron = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6") 
# NeuN is FOX3 - RBFOX3
# PSD95 also SP-90 or DLG4
# VGLUT2 is SLC17A6
DoHeatmap(seu.q, features = mat_neuron, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
# cluster 4 also show mature neuron markers
DotPlot(seu.q, features = mat_neuron)+RotatedAxis()
# excitatory neuron markers
ex = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
DoHeatmap(seu.q, features = ex, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = ex)+RotatedAxis()
# inhibitory neuron markers
inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
DoHeatmap(seu.q, features = inh, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = inh)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 

### glia markers
microglia = c("PTPRC","AIF1","ADGRE1")  # ADGRE1 is a microglia marker F4/80, CD45 is PTPRC, gene name IBA1 is AIF1
astolgNPCpromicro = c("GFAP","S100B","SLC1A2","MBP","SOX10","SPP1","DCX","NEUROD1","TBR1","PCNA","MKI67","PTPRC","AIF1","ADGRE1")
# note GLT1 is EAAT2 which is SLC1A2 glutatmate transporter
# epithelial
epi = c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1") # e-cadherin is CDH1

DoHeatmap(seu.q, features = astolgNPCpromicro, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = astolgNPCpromicro, group.by = 'RNA_snn_res.0.6')+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 
DoHeatmap(seu.q, features = epi, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = epi, group.by = 'RNA_snn_res.0.6')+RotatedAxis()

# also add Radial glia marker overlap with Glia and Neurons

features <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
              "ITGB5","IL6ST")
DoHeatmap(seu.q, features = features, size=3, angle =90, group.bar.height = 0.02, group.by = 'RNA_snn_res.0.6')
DotPlot(seu.q, features = features, group.by = 'RNA_snn_res.0.6')+RotatedAxis()


```


Label the Neuron2 FACS population 

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.6'
cluster.ids <- c("Neurons1","immatureNeurons1","Neurons2",
                 "Other","DAneurons1","DAneurons2","immatureNeurons2",
                 "DAneurons3","RG","immatureNeurons1","Epithelial","Endothelial")
unique(seu.q$RNA_snn_res.0.6)

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$subgroups <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'subgroups', repel = TRUE)


# label again with just numbering
# then I'll find markers in pairs to distinguish groups. 

Idents(seu.q) <- 'RNA_snn_res.0.6'
cluster.ids <- c("Neurons1","Neurons2","Neurons3",
                 "Other","DAneurons1","DAneurons2","Neurons4",
                 "DAneurons3","RG","Neurons2","Epithelial","Endothelial")
unique(seu.q$RNA_snn_res.0.6)

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$number.groups <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'number.groups', repel = TRUE)


saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Neurons2LabelsSeu30092022.RDS")



```

Proportions of cell types

```{r}



```






Find markers in pairs to go back and classify the subgroups.
Will need to return to this for Neurons1 FACS

Neurons 5 and Neurons2 had similar markers and were merged
Subset again

```{r}

# faster to make a subset objects of only neurons and use find all markers

neuron.sub <- subset(seu.q, idents = c("Neurons1","Neurons2","Neurons3",
                                       "Neurons4"))

neuron.sub.markers <- FindAllMarkers(neuron.sub)

top5 <- neuron.sub.markers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(neuron.sub, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)

DotPlot(neuron.sub, features = top5$gene) + RotatedAxis()


# neurons 5 was joined to Neurons 2
# cluster markers done again

# Neurons1 : MGP (targeting neural projections BMP signaling), HPD (excitatory and inibitory, could be cell adhesion or apoptosis), MSX1 (transcription factor BMP signaling, midbrain marker, developmental), CYP1B1 (redox homeostatis)   
# MGP, HPD, MSX1, CYP1B1

# there isn't a good proportion of cells expression any of the markers, Neurons2 has the best amount

# Neurons2: TFP12 serine protease melatonin conversion, PTN (cytokine signaling), LYgH (inhances nAChRs), S100A10 (modulates serotonin receptor), IFI27 (antiviral activity)
#TFP12,PTN, LY6H, S100A10, IFI27 

# Neurons3: SOX4, ASCL1 (neurogenesis),

# SOX4 is a marker of 3 but has high expression in 4 as well 

# Neurons4: PCAT4 (not noted in neurons), TPH1 (5HT synthesis), GK5 (neuronal maintainance), SST (somatostatin, GABA spike regulation), TTR (neural protective in AD)
# PCAT4, TPH1, GK5, SST, TTR

# markers to use

# Neurons1: MSX1, CYP1B1      
# Neurons2: LY6H, S100A10     
# Neurons3: SOX4, ASCL1 (Neurogenesis) Immature
# Neurons4: GK5, SST                         More mature



# Maybe neurons 1 and 2 could be merged

# lets see how the markers would look

neurons.1and2 <- FindMarkers(neuron.sub, ident.1 = c("Neurons1","Neurons2"),
                             ident.2 = c("Neurons3","Neurons4"))

top10 <- neurons.1and2 %>% top_n(n=10, wt = avg_log2FC)
ft.up <- rownames(top10) # up in Neurons1 and 3
top10 <- neurons.1and2 %>% top_n(n=-10, wt = avg_log2FC)
ft.down <- rownames(top10)
features <- c(ft.up,ft.down)

DoHeatmap(neuron.sub, features = features, size=3, angle =90, group.bar.height = 0.02)

DotPlot(neuron.sub, features = features) + RotatedAxis()

# all the markers were up regulated in neurons2 and not really neurons1
# I'll keep them separated


```


Use subgrouping and find cluster markers to look at neuronal subtypes.

```{r}

# faster to make a subset objects of only neurons and use find all markers

neuron.sub <- subset(seu.q, idents = c("DAneurons1","DAneurons2","DAneurons3"))

neuron.sub.markers <- FindAllMarkers(neuron.sub)

top5 <- neuron.sub.markers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(neuron.sub, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)

DotPlot(neuron.sub, features = top5$gene) + RotatedAxis()

# markers are much clearer for the DA neuron subgroups

# DA neurons 1: WIF1, CYP1B1, IGFBP3, HPD, WFIKKN2
# WIF1 (secreted WNT inhibitor, promotes regeneration), CYP1B1 (redox homeostatis), IGFBP3 (prolactin secretion regulation hypothalmus), HPD (neuro protective), WFIKKN2 (Receptor for TNC)
# DA neurons2: CDH7, RUNX1T1, ASCL1, DLK1, MEG3
# CDH7 (neuro circuitry development, SEMA), RUNX1T1 (neuronal differentiation), ASCL1 (neuronal differentiation), DLK1 (neural differentation), MEG3 (neural homeostatis)  
# DA neurons3: PCAT4, NEUROD1, NCKAP5, GK5, SST
# PCAT4 (dendritic growth), NEUROD1 (neural differentation), NCKAP5 (Excitory neurons), GK5 (TH ), SST (regualates spike times)

# DA neurons1: CYP1B1, IGFBP3
# DA neurons2: RUNX1T1, ASCL1
# DA neurons3: NEUROD1, NCKAP5


```


Name the Neurons2 FACS population with the Neuron subtype latter.

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.6'
cluster.ids <- c("Neurons-MSX1","Neurons-LY6H","Neurons-ASCL1",
                 "Other","DAneurons-CYP1B1","DAneurons-ASCL1","Neurons-GK5",
                 "DAneurons-NEUROD1","RG","Neurons-LY6H","Epithelial","Endothelial")

unique(seu.q$RNA_snn_res.0.6)

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$cellsubgroups <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'cellsubgroups', repel = TRUE)


```

```{r}

# save the Neurons2 with labels

saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Neurons2LabelsSeu30092022.RDS")

```


FACS population Glia1 (should be astrocytes)

```{r}
# explore filtering
seu <- Glia1
seu
# 
VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)
VlnPlot(seu, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)

# filter more cells

seu.ft <- subset(seu, subset = nFeature_RNA > 300 & nCount_RNA > 500 & nCount_RNA < 10000) 
seu.ft

# still a lot of cells 47295
# will likely remove a lot more with the doublet finder


```

```{r}

saveRDS(seu.ft, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1AstroSeu01102022.RDS")

seu.ft <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1AstroSeu01102022.RDS")

seu.ft <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1AstroSeu01102022.RDS")

```

Doublet finder

```{r}

suppressMessages(require(DoubletFinder))

# filtering out MALAT1 and mitochondrial genes

seu.ft <- seu.ft[!grepl("MALAT1", rownames(seu.ft)), ]
seu.ft <- seu.ft[!grepl("^MT-", rownames(seu.ft)), ]

# like in the tutorial I'm following MALAT1 is the top most expressed gene.  The top genes are a lot of MT and Ribosomal genes

seu.ft[["percent.rb"]] <- PercentageFeatureSet(seu.ft, pattern = "^RP")

# down sample there are too many cells to run doublet finder
seu.sub <- subset(seu.ft, downsample = 20000)

seu.d = NormalizeData(seu.sub)
seu.d = FindVariableFeatures(seu.d, verbose = F)
seu.d = ScaleData(seu.d, vars.to.regress = c("nFeature_RNA", "percent.mt"),
    verbose = F)
seu.d = RunPCA(seu.d, verbose = F, npcs = 15)
seu.d = RunUMAP(seu.d, dims = 1:10, verbose = F)

nExp <- round(ncol(seu.d) * 0.15)  # expect more doublets because there is a lot more cells
seu.d <- doubletFinder_v3(seu.d, pN = 0.25, pK = 0.09, nExp = nExp, PCs = 1:10)
# the memory limit is reached here - I could run on compute canada
# For now I'll downsample
# this works

# name of the DF prediction can change, so extract the correct column name.
DF.name = colnames(seu.d@meta.data)[grepl("DF.classification", colnames(seu.d@meta.data))]


cowplot::plot_grid(ncol = 2, DimPlot(seu.d, group.by = "orig.ident") + NoAxes(),
    DimPlot(seu.d, group.by = DF.name) + NoAxes())

VlnPlot(seu.d, features = "nFeature_RNA", group.by = DF.name, pt.size = 0.1)

```




Remove the doublet cells

```{r}
seu.d <- seu.d[, seu.d@meta.data[, DF.name]== "Singlet"]
dim(seu.d)
dim(seu.sub)

# 20000 pre filter
# creates the expected percentage

```

Repeat workflow with doublet removed data and find clusters for 

```{r}
seu <- NormalizeData(seu.d, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
seu <- ScaleData(seu)
seu <- RunPCA(seu)
seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 43, dims = 1:30)
DimPlot(seu, reduction = "umap")

seu.q <- FindNeighbors(seu, dims = 1:25, k.param = 43)
seu.q <- FindClusters(seu.q, resolution = c(0,0.2,0.4,0.6))
seu.q <- FindClusters(seu.q, resolution = c(0,0.05,0.1,0.8))
library(clustree)
clustree(seu.q)
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.05')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.1')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.2')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.4')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.6')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.8')




```
Look at some expression markers in a feature plot

```{r}
# genes reported up in Astrocytes
FeaturePlot(seu.q, features = c("GFAP","S100B","AQP4","SLC1A3","GJA1",
                                "APOE","TEAD1","GSTA4","SOX9",
                                "VIM","HMG20A","ALDH1L1"))
# almost no GFAP expression and lots of S100B everywhere

```



Predict cell types

```{r}


# SNCA and control midbrain organoids 165 days in culture
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")

# Midbrain  AIW002 120 days in culture
AIWMBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")

# Midbrain AIW002 60 days in culture

AIW60 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")


#first predict with the MBO data
Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"

# find the reference anchors
print("finding reference anchors")
anchors <- FindTransferAnchors(reference = MBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = MBO$cluster_labels)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAST23.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAST23.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.8, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# clusters don't break up by the predicted cell types

############ another predictions now using the AIW organoids

Idents(AIWMBO) <- "res08names"
DefaultAssay(AIWMBO) <- "RNA"

anchors <- FindTransferAnchors(reference = AIWMBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIWMBO$res08names)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAIW.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAIW.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.8, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# the predicted cell types make more sense from the AIW002 organoid
# now predict with the AIW002 60 days organoid

Idents(AIW60) <- "cluster.ids"
DefaultAssay(AIW60) <- "RNA"

anchors <- FindTransferAnchors(reference = AIW60, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIW60$cluster.ids) 
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW60.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.8, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# save ojbect with predicitons
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1PredictionsSeu01102022.RDS")



```


Look at the reference objects 

```{r}

# triplication
DimPlot(MBO) # has two groups of astrocytes
# AIW 60 days
DimPlot(AIW60) # has one group of astrocytes
# AIW 120 days
DimPlot(AIWMBO) # has two astrocyte groups


```

Look more at the predictions

```{r}

# AIW002 120 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW120 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw120 <- top.pred.celltype.AIW120[order(top.pred.celltype.AIW120$Var1,-top.pred.celltype.AIW120$Freq),]
row.names(df.top.aiw120) <- NULL
df.top.aiw120$I <- row.names(df.top.aiw120)

# AIW002 60 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW60 <-as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw60 <- top.pred.celltype.AIW60[order(top.pred.celltype.AIW60$Var1,-top.pred.celltype.AIW60$Freq),]
row.names(df.top.aiw60) <- NULL
df.top.aiw60$I <- row.names(df.top.aiw60)


# AST23 165 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AST23 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.AST23 <- top.pred.celltype.AST23[order(top.pred.celltype.AST23$Var1,-top.pred.celltype.AST23$Freq),]
row.names(df.top.AST23) <- NULL
df.top.AST23$I <- row.names(df.top.AST23)

pred.table <- merge(df.top.AST23, df.top.aiw60, by = 'I', all = TRUE)
pred.table <- merge(pred.table, df.top.aiw120, by = 'I')
pred.table

```
These predictions are not good.  There are several astrocyte markers by expression levels.  Everything is predicted as Radial glia or oligo dendrocytes


Try to predict with the astrocyte Kamath data

```{r}

astro.ref <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/PD_astro.Rds")
# need to make PCA and UMAP
astro.ref <- NormalizeData(astro.ref)
astro.ref <- FindVariableFeatures(astro.ref, selection.method = "vst", nfeatures = 2000)
astro.ref <- ScaleData(astro.ref)
astro.ref <- RunPCA(astro.ref)
astro.ref <- RunUMAP(astro.ref, reduction = "pca", n.neighbors = 205, dims = 1:25)

colnames(astro.ref@meta.data)


Idents(astro.ref) <- "Cell_Subtype"
DefaultAssay(astro.ref) <- "RNA"

# find the reference anchors
print("finding reference anchors")
anchors <- FindTransferAnchors(reference = astro.ref ,query = seu.q, dims = 1:20)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = astro.ref$Cell_Subtype, k.weight = 10)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$astro.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'astro.pred', label = TRUE)
table(seu.q$astro.pred)

seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, NA)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
seu.q$astro.pred.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'astro.pred.thresh', label = TRUE)
table(seu.q$astro.pred.thresh)

# 19986 Astro_VIM_TNFSRF12A no threshold      Astro_GLYATL2 14
# 8376 Astro_VIM_TNFSRF12A   with 95% threshold


t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$astro.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.astro[order(top.pred.astro$Var1,-top.pred.astro$Freq),]
row.names(df.top.astro) <- NULL


t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$astro.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.astro[order(top.pred.astro$Var1,-top.pred.astro$Freq),]
row.names(df.top.astro) <- NULL



```





Look at cluster markers

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.2'
ClusterMarkers <- FindAllMarkers(seu.q, only.pos = TRUE)

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)

write.csv(ClusterMarkers,"/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/Glia1AstrocytesClusterMarkers_new.csv")

# for the res 0.6 the largers groups 0 and 1 don't have great markers and none of the markers are really very good.  
# I'll rerun with res 0.2
unique(seu.q$RNA_snn_res.0.2)

# still not much better



```



Check cell type markers with EnrichR

```{r}

library(enrichR)

setEnrichrSite("Enrichr") # Human genes
# list of all the databases

# libaries with cell types

db <- c('Allen_Brain_Atlas_up','Descartes_Cell_Types_and_Tissue_2021',
        'CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')

# enrichr(genes, databases = NULL)

#I'll run the clusters one at a time

N1.c0 <- ClusterMarkers %>% filter(cluster == 5 & avg_log2FC > 0)
genes <- N1.c0$gene

N1.c0.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c0.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[4]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")

N1.Er.genes.1 <- N1.c0.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c0.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c0.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3


N1.Er.genes.4 <- N1.c0.Er[[4]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.4



# cluster 0 - Cell type marker library - Brain astrocyte top hit and embryonic astrocytes
# gene list in term: brain astrocyte	EFEMP1;NFIA;LIX1;PSAP;KIF21A;S100B;CRYAB;DKK3
# embryo astrocytes	SOX2;BEX1;HMGCS1;PTPRZ1;LIX1;S100B;DKK3;ITM2C

# cluster 1 - stem cell pericyte (brain), stelate, astrocyte

# cluster 2 - hypothalmus, endothelial cells, macrophage
# endothelial CSTB;PRELID1;MT1X;CRIP2;RHOC;TMEM141;MT2A;RPS28;CCDC85B;EIF3I;RBP1;ID1;C4ORF3;ID3;PCBD1;MSX1;PPIC

# cluster 3 - smooth muscle cells

# cluster 4 - NK cells, fibroblasts
# NK cells ITGB1;RAB5C;GSTP1;PDCD5;EEF1B2;TGOLN2;SDCBP;MT2A;LDHA;SNRPD2;YWHAQ;ZNF326;TMSB10;CCDC50
# fibroblasts 	COL3A1;CALD1;COL6A3

# cluster 5 - endothelial cells, NK cells, CD8+

# cluster 6 - stromal cells eurythroblasts, none-neuronal, oligo

# reran and now there are only 5 clusters
# repeat checking


```

Cluster 0 - astrocytes
Cluster 1 - pericyte astrocyte (weak still)
Cluster 2 - endothelial
Cluster 3 - smooth muscle
Cluster 4 - NK/fibroblast
Cluster 5 - endothelial
Cluster 6 - non- neuronal







```{r}

VlnPlot(seu.q, features = c("CD44","ITGB1","S100B"), group.by = 'orig.ident')
VlnPlot(seu.ft, features = c("CD44","ITGB1","S100B"), group.by = 'orig.ident')

```

Check expression of known markers

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.2'

feature_list = c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")

DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = feature_list) +RotatedAxis()

PD_poulin = c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")

DoHeatmap(seu.q, features = PD_poulin, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = PD_poulin)+RotatedAxis()

ealryNeur = c("DCX","NEUROD1","TBR1")
proliferation = c("PCNA","MKI67")
neuralstem = c("SOX2","NES","PAX6","MASH1")

feature_list <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = feature_list)+RotatedAxis()


mat_neuron = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6") 
# NeuN is FOX3 - RBFOX3
# PSD95 also SP-90 or DLG4
# VGLUT2 is SLC17A6
DoHeatmap(seu.q, features = mat_neuron, size=3, angle =90, group.bar.height = 0.02)
# cluster 4 also show mature neuron markers
DotPlot(seu.q, features = mat_neuron)+RotatedAxis()
# excitatory neuron markers
ex = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
DoHeatmap(seu.q, features = ex, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = ex)+RotatedAxis()
# inhibitory neuron markers
inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
DoHeatmap(seu.q, features = inh, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = inh)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 

### glia markers
microglia = c("PTPRC","AIF1","ADGRE1")  # ADGRE1 is a microglia marker F4/80, CD45 is PTPRC, gene name IBA1 is AIF1
astolgNPCpromicro = c("GFAP","S100B","SLC1A2","MBP","SOX10","SPP1","DCX","NEUROD1","TBR1","PCNA","MKI67","PTPRC","AIF1","ADGRE1")
# note GLT1 is EAAT2 which is SLC1A2 glutatmate transporter
# epithelial
epi = c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1") # e-cadherin is CDH1

DoHeatmap(seu.q, features = astolgNPCpromicro, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = astolgNPCpromicro)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 
DoHeatmap(seu.q, features = epi, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = epi)+RotatedAxis()

# also add Radial glia marker overlap with Glia and Neurons

features <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
              "ITGB5","IL6ST")
DoHeatmap(seu.q, features = features, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = features)+RotatedAxis()




```

No TH expression

clusters 5 VIM highest, S100 B 
Cluster 4 
Cluster 3 has some cells with high OTX2, NES indicates NPC/Precursors
Cluster 2 has some SOX2 and PAX6 indicates NPC, VAMP2 indicating neurons, S100B highest and most - indicates astrocytes, also MBP indicates oligos
Cluster 1
Cluster 0 


Lable the clusters 

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.2'

#seu.q <- BuildClusterTree(seu.q, reorder = TRUE, reorder.numeric = TRUE)
unique(seu.q$RNA_snn_res.0.2)

cluster.ids <- c("Astrocytes1","Astrocytes2","Precursors","RG1","RG2","Endothelial")

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$subgroups <- Idents(seu.q)

#DimPlot(seu.q, group.by = 'RNA_snn_res.0.2', label = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'subgroups', repel = TRUE)

# something weird is going on in the order

#saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1LabledSeu301102022.RDS")


```



Compare the Astrocyte groups and get some markers for sub groups

```{r}


astro.sub.markers <- FindMarkers(seu.q, ident.1 = "Astrocytes1", ident.2 = "Astrocytes2", only.pos = FALSE)
#top5 <- astro.sub.markers %>% top_n(n=5, wt = avg_log2FC)

DoHeatmap(seu.q, features = c("PLCG2","PTPRZ1","SNHG25","VCAN","LUM","DCN","S1004A"), size=3, angle =90, group.bar.height = 0.02)

astro2 <- rownames(astro.sub.markers %>% filter(avg_log2FC < 0.05))

DotPlot(seu.q, features = c("PLCG2","PTPRZ1","SNHG25","VCAN","LUM","DCN","S1004A")) + RotatedAxis()

DoHeatmap(seu.q, features = astro2[1:15], size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = astro2[1:15]) +RotatedAxis()


# radial glia subtyping
Idents(seu.q) <- ('subgroups')
rg.sub.markers <- FindMarkers(seu.q, ident.1 = "RG1", ident.2 = "RG2", only.pos = FALSE)
top5.up <- rg.sub.markers %>% top_n(n=10, wt = avg_log2FC)
top5.down <- rg.sub.markers %>% top_n(n=-10, wt = avg_log2FC)
ft <- rownames(top5.up)

DoHeatmap(seu.q, features = ft, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = ft) + RotatedAxis()


ft <- rownames(top5.down)

DoHeatmap(seu.q, features = ft, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = ft) + RotatedAxis()

npc.markers <- FindMarkers(seu.q, ident.1 = "Precursors", ident.2 = c("Astrocytes2","Astrocytes1"), only.pos = FALSE)
top10.npc <- npc.markers %>% top_n(n=10, wt = avg_log2FC)
npc.markers <- npc.markers %>% filter(avg_log2FC > 0)
dim(npc.markers)

ft <- rownames(top10.npc)
# consider naming precursors as astrocytes3
DoHeatmap(seu.q, features = ft, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = ft) + RotatedAxis()


npc.markers.rg <- FindMarkers(seu.q, ident.1 = "Precursors", ident.2 = c("RG2","RG1"), only.pos = TRUE)
top10.npc <- npc.markers.rg %>% top_n(n=10, wt = avg_log2FC)

ft <- rownames(top10.npc)
# consider naming precursors as astrocytes3
DoHeatmap(seu.q, features = ft, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = ft) + RotatedAxis()

## these have more differences from RG than Astrocytes


```

Add subtype gene ids

```{r}

DimPlot(seu.q, group.by = 'RNA_snn_res.0.2')

cluster.ids <- c("Astrocytes-PLCG2","Astrocytes-DNC","Astrocytes-IGFBP2",
                 "RG1-CDKN1C","RG2-TYRP1","Endothelial")
#cluster.ids <- c("Astrocytes1","Astrocytes2","Precursors(Astrocytes)","RG1","RG2","Endothelial")

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Cell_types <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Cell_types', repel = TRUE)


saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1LabledSeu301102022.RDS")

# label with main cell type groups 

DimPlot(seu.q, group.by = 'RNA_snn_res.0.2')

cluster.ids <- c("Astrocytes","Astrocytes","Astrocytes",
                 "RG","RG","Endothelial")
#cluster.ids <- c("Astrocytes1","Astrocytes2","Precursors(Astrocytes)","RG1","RG2","Endothelial")

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Cell_Type <- Idents(seu.q)

DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Cell_Type', repel = TRUE)


saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia1LabledSeu301102022.RDS")




```


Proportions of cells types

```{r}



```


Quick check the Glial2 

```{r}

# explore filtering
seu <- Glia2
seu
# 
VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)

VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 1000)
VlnPlot(seu, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 500)
VlnPlot(seu, pt.size = 0.10, features = c("nCount_RNA"), y.max = 2000)

# filter more cells

seu.ft <- subset(seu, subset = nFeature_RNA > 250 & nCount_RNA > 250 & nCount_RNA < 10000) 
seu.ft

VlnPlot(seu.ft, pt.size = 0.10, features = c("nFeature_RNA"), y.max = 2000)

```
```{r}

VlnPlot(seu.ft.glia, features = c("CD44","S100B","ITGB1"))
VlnPlot(seu.ft, features = c("CD44","S100B","ITGB1"), group.by = 'orig.ident')
# both glia populations are similar


```

Levels seem similar in Glia1 and Glia2

Remove doublets and start to process Glia 2

```{r}

suppressMessages(require(DoubletFinder))

# filtering out MALAT1 and mitochondrial genes

seu.ft <- seu.ft[!grepl("MALAT1", rownames(seu.ft)), ]
seu.ft <- seu.ft[!grepl("^MT-", rownames(seu.ft)), ]

# like in the tutorial I'm following MALAT1 is the top most expressed gene.  The top genes are a lot of MT and Ribosomal genes

seu.ft[["percent.rb"]] <- PercentageFeatureSet(seu.ft, pattern = "^RP")

seu.d = NormalizeData(seu.ft)
seu.d = FindVariableFeatures(seu.d, verbose = F)
seu.d = ScaleData(seu.d, vars.to.regress = c("nFeature_RNA", "percent.mt"),
    verbose = F)
seu.d = RunPCA(seu.d, verbose = F, npcs = 15)
seu.d = RunUMAP(seu.d, dims = 1:10, verbose = F)

nExp <- round(ncol(seu.d) * 0.08)  # expect more doublets because there is a lot more cells
seu.d <- doubletFinder_v3(seu.d, pN = 0.25, pK = 0.09, nExp = nExp, PCs = 1:10)
# the memory limit is reached here - I could run on compute canada
# For now I'll downsample
# this works

# name of the DF prediction can change, so extract the correct column name.
DF.name = colnames(seu.d@meta.data)[grepl("DF.classification", colnames(seu.d@meta.data))]


cowplot::plot_grid(ncol = 2, DimPlot(seu.d, group.by = "orig.ident") + NoAxes(),
    DimPlot(seu.d, group.by = DF.name) + NoAxes())

VlnPlot(seu.d, features = "nFeature_RNA", group.by = DF.name, pt.size = 0.1)

```


```{r}

seu.d <- seu.d[, seu.d@meta.data[, DF.name]== "Singlet"]
dim(seu.d)
dim(seu.ft)



```

Cluster 

```{r}
seu <- NormalizeData(seu.d, normalization.method = "LogNormalize", scale.factor = 10000)
seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2000)
seu <- ScaleData(seu)
seu <- RunPCA(seu)
seu <- RunUMAP(seu, reduction = "pca", n.neighbors = 25, dims = 1:30)
DimPlot(seu, reduction = "umap")

seu.q <- FindNeighbors(seu, dims = 1:25, k.param = 25)
seu.q <- FindClusters(seu.q, resolution = c(0,0.05,0.2,0.4,0.5,0.6,0.8))
library(clustree)

DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.05')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.1')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.2')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.4')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.6')
DimPlot(seu.q, reduction = "umap", group.by = 'RNA_snn_res.0.8')
clustree(seu.q)
# 0.4 is likely the best annotate subgroups


```

Predict cell types 


```{r}

# SNCA and control midbrain organoids 165 days in culture
MBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AST23_BrainComm/MBOclusters_names29072021.rds")

# Midbrain  AIW002 120 days in culture
AIWMBO <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio120days/MOintegratedClusterK123res0.8.names_nov16_2021")

# Midbrain AIW002 60 days in culture

AIW60 <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/AIWtrio60days/AWI002ParkinKOPinkKO60days_labels_14052022.rds")


#first predict with the MBO data
Idents(MBO) <- "cluster_labels"
DefaultAssay(MBO) <- "RNA"

# find the reference anchors
print("finding reference anchors")
anchors <- FindTransferAnchors(reference = MBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = MBO$cluster_labels)
seu.q <- AddMetaData(seu.q, metadata = predictions)

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$MBOAST23.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'MBOAST23.pred', label = TRUE)

# see how accurate the predictions are
seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "None")

Idents(seu.q) <- 'predicted.id'
seu.q$MBOAST23.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'predicted.id', label = TRUE)
table(seu.q$MBOAST23.pred)
table(seu.q$MBOAST23.thresh)


 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.4, seu.q$MBOAST23.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# clusters don't break up by the predicted cell types

############ another predictions now using the AIW organoids

Idents(AIWMBO) <- "res08names"
DefaultAssay(AIWMBO) <- "RNA"

anchors <- FindTransferAnchors(reference = AIWMBO ,query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIWMBO$res08names)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW120.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW120.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.4, seu.q$MBOAIW.pred))
t.lables$Freq <- as.double(t.lables$Freq)
# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# see how accurate the predictions are
seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "None")

Idents(seu.q) <- 'predicted.id'
seu.q$AIW120.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW120.thresh', label = TRUE)
table(seu.q$AIW120.pred)
table(seu.q$AIW120.thresh)

# the predicted cell types make more sense from the AIW002 organoid
# now predict with the AIW002 60 days organoid

Idents(AIW60) <- "cluster.ids"
DefaultAssay(AIW60) <- "RNA"

anchors <- FindTransferAnchors(reference = AIW60, query = seu.q, dims = 1:25)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = AIW60$cluster.ids) 
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$AIW60.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.pred', label = TRUE)
 
## check the proportion of cell types predicted in each cluster
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.4, seu.q$AIW60.pred))
t.lables$Freq <- as.double(t.lables$Freq)

# try bar chart
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity")

# see how accurate the predictions are
seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "None")

Idents(seu.q) <- 'predicted.id'
seu.q$AIW60.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'AIW60.thresh', label = TRUE)
table(seu.q$AIW60.pred)
table(seu.q$AIW60.thresh)



# most of the cells are predicted as NPCs in many populations



```

```{r}
# save with predictions so far
#saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")

seu.q <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")


```


See how many cells are predicted as astrocytes with the threshold

```{r}

DAsubtypes <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/DAsubgroups_processed.Rds")

Idents(astro.ref) <- "Cell_Subtype"
DefaultAssay(astro.ref) <- "RNA"

# find the reference anchors
print("finding reference anchors")
anchors <- FindTransferAnchors(reference = DAsubtypes ,query = seu.q, dims = 1:20)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = astro.ref$Cell_Subtype, k.weight = 10)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$astro.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'astro.pred', label = TRUE)
table(seu.q$astro.pred)

seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "none")
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
seu.q$astro.pred.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'astro.pred.thresh', label = TRUE)
table(seu.q$astro.pred.thresh)


t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$astro.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.astro[order(top.pred.astro$Var1,-top.pred.astro$Freq),]
row.names(df.top.astro) <- NULL


t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$astro.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.astro[order(top.pred.astro$Var1,-top.pred.astro$Freq),]
row.names(df.top.astro) <- NULL

# a lot of these cells are also getting labelled as astrocytes


```

Do these get labelled as DA neurons too???

```{r}
seu.q <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")

```



```{r}

DAsubtypes <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/Macosko_Data/DAsubgroups_processed.Rds")
Idents(DAsubtypes) <- "Cell_Subtype"
da.ref <- subset(DAsubtypes, downsample = 500)

# find the reference anchors
print("finding reference anchors")
anchors <- FindTransferAnchors(reference = da.ref, query = seu.q, dims = 1:20)
print("getting predictions")
predictions <- TransferData(anchorset = anchors, refdata = da.ref$Cell_Subtype, k.weight = 10)
seu.q <- AddMetaData(seu.q, metadata = predictions)
print(table(seu.q$predicted.id))

Idents(seu.q) <- 'predicted.id'
# add new dataslot for MBO predicted ID to make the next prediction
seu.q$da.pred <- Idents(seu.q)
DimPlot(seu.q, group.by = 'da.pred', label = TRUE)
table(seu.q$da.pred)

seu.q$predicted.id <- ifelse(seu.q$prediction.score.max > 0.95, seu.q$predicted.id, "none")

Idents(seu.q) <- 'predicted.id'
seu.q$da.pred.thresh <- Idents(seu.q)
DimPlot(seu.q, group.by = 'da.pred.thresh', label = TRUE)
table(seu.q$da.pred.thresh)


t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$da.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.astro[order(top.pred.astro$Var1,-top.pred.da$Freq),]
row.names(df.top.astro) <- NULL


t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$astro.pred))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.astro[order(top.pred.astro$Var1,-top.pred.astro$Freq),]
row.names(df.top.astro) <- NULL


# after thresholding very few cells are predicted as neurons



```

Compare predictions - make a predictions table

```{r}

# AIW002 120 days predictions - take the thresholded options
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$AIW120.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW120 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw120 <- top.pred.celltype.AIW120[order(top.pred.celltype.AIW120$Var1,-top.pred.celltype.AIW120$Freq),]
row.names(df.top.aiw120) <- NULL
df.top.aiw120$I <- row.names(df.top.aiw120)

# AIW002 60 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$AIW60.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AIW60 <-as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.aiw60 <- top.pred.celltype.AIW60[order(top.pred.celltype.AIW60$Var1,-top.pred.celltype.AIW60$Freq),]
row.names(df.top.aiw60) <- NULL
df.top.aiw60$I <- row.names(df.top.aiw60)


# AST23 165 days predictions
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$MBOAST23.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.AST23 <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.AST23 <- top.pred.celltype.AST23[order(top.pred.celltype.AST23$Var1,-top.pred.celltype.AST23$Freq),]
row.names(df.top.AST23) <- NULL
df.top.AST23$I <- row.names(df.top.AST23)

# add the threshold Astro predictions 
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$astro.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.astro <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.astro <- top.pred.celltype.astro[order(top.pred.celltype.astro$Var1,-top.pred.celltype.astro$Freq),]
row.names(df.top.astro) <- NULL
df.top.astro$I <- row.names(df.top.astro)

# add the neurons predictions 
t.lables <- as.data.frame(table(seu.q$RNA_snn_res.0.2, seu.q$da.pred.thresh))
t.lables$Freq <- as.double(t.lables$Freq)
ggplot(t.lables, aes(y = Freq, x = Var1, fill = Var2)) + geom_bar(position = "stack", stat= "identity") + RotatedAxis() 
top.pred.celltype.da <- as.data.frame(t.lables  %>% group_by(Var1)  %>% top_n(2, Freq))
df.top.da <- top.pred.celltype.da[order(top.pred.celltype.da$Var1,-top.pred.celltype.da$Freq),]
row.names(df.top.da) <- NULL
df.top.da$I <- row.names(df.top.da)

pred.table <- merge(df.top.AST23, df.top.aiw60, by = 'I', all = TRUE)
pred.table <- merge(pred.table, df.top.aiw120, by = 'I')
pred.table <- merge(pred.table, df.top.astro, by = 'I')
pred.table <- merge(pred.table, df.top.da, by = 'I')

pred.table


```

Predicted cluster annotations
0	Unknown/ NPC
1	RG
2	astro
3	RG
4	neurons
5	RG

Look at gene lists with known markers

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.2'

# many cell types list
feature_list = c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")

DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = feature_list) +RotatedAxis()

# Dopaminergic markers
PD_poulin = c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")

DoHeatmap(seu.q, features = PD_poulin, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = PD_poulin)+RotatedAxis()

ealryNeur = c("DCX","NEUROD1","TBR1")
proliferation = c("PCNA","MKI67")
neuralstem = c("SOX2","NES","PAX6","MASH1")

feature_list <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
DoHeatmap(seu.q, features = feature_list, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = feature_list)+RotatedAxis()


mat_neuron = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6") 
# NeuN is FOX3 - RBFOX3
# PSD95 also SP-90 or DLG4
# VGLUT2 is SLC17A6
DoHeatmap(seu.q, features = mat_neuron, size=3, angle =90, group.bar.height = 0.02)
# cluster 4 also show mature neuron markers
DotPlot(seu.q, features = mat_neuron)+RotatedAxis()
# excitatory neuron markers
ex = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
DoHeatmap(seu.q, features = ex, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = ex)+RotatedAxis()
# inhibitory neuron markers
inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
DoHeatmap(seu.q, features = inh, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = inh)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 

### glia markers
microglia = c("PTPRC","AIF1","ADGRE1")  # ADGRE1 is a microglia marker F4/80, CD45 is PTPRC, gene name IBA1 is AIF1
astolgNPCpromicro = c("GFAP","S100B","SLC1A2","MBP","SOX10","SPP1","DCX","NEUROD1","TBR1","PCNA","MKI67","PTPRC","AIF1","ADGRE1")
# note GLT1 is EAAT2 which is SLC1A2 glutatmate transporter
# epithelial
epi = c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1") # e-cadherin is CDH1

DoHeatmap(seu.q, features = astolgNPCpromicro, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = astolgNPCpromicro)+RotatedAxis()
# cluster 4 is more excitatory than inhbitory but neither marker set has much expression 
DoHeatmap(seu.q, features = epi, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = epi)+RotatedAxis()

# also add Radial glia marker overlap with Glia and Neurons

features <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
              "ITGB5","IL6ST")
DoHeatmap(seu.q, features = features, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = features)+RotatedAxis()

# radial glia markers
rg <- c("VIM","NES","PAX6","HES1","EAAT1","NCAD1","SOX2","FABP7")
DoHeatmap(seu.q, features = rg, size=3, angle =90, group.bar.height = 0.02)
DotPlot(seu.q, features = rg)+RotatedAxis()

# NPC and radial glia are very similar

```
Marker expression predictions
Cluster 0 - unknown
Cluster 1 - RG
Cluster 2 - unknown
Cluster 3 - RG
cluster 4 - immature neurons
Cluster 5 - RG, opc


Check the levels of RNA in each cluster 

```{r}
VlnPlot(seu.q, features = "nFeature_RNA")

```
Cluster 0 and 2 have fewer sequences than other groups and thus no markers
Possibly remove these is they don't come up with some markers

Find cluster markers

```{r}
Idents(seu.q) <- 'RNA_snn_res.0.2'
ClusterMarkers <- FindAllMarkers(seu.q, only.pos = TRUE)

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)

#write.csv(ClusterMarkers,"/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/Glia2RGClusterMarkers_new.csv")

Idents(seu.q) <- 'RNA_snn_res.0.1'
ClusterMarkers <- FindAllMarkers(seu.q, only.pos = TRUE)

top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt = avg_log2FC)
DoHeatmap(seu.q, features = top5$gene, size=3, angle =90, group.bar.height = 0.02)


```
Markers of 2 are matching with 5 possibly merge these together
Cluster 0 markers don't look up regulated but the list is long

Look at the libraries

```{r}
library(enrichR)

setEnrichrSite("Enrichr") # Human genes
# list of all the databases

# libaries with cell types

db <- c('Descartes_Cell_Types_and_Tissue_2021',
        'CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')

# enrichr(genes, databases = NULL)

#I'll run the clusters one at a time

N1.c0 <- ClusterMarkers %>% filter(cluster == 0 & avg_log2FC > 0)
genes <- N1.c0$gene

N1.c0.Er <- enrichr(genes, databases = db)
plotEnrich(N1.c0.Er[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")
plotEnrich(N1.c0.Er[[3]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value")


N1.Er.genes.1 <- N1.c0.Er[[1]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.1

N1.Er.genes.2 <- N1.c0.Er[[2]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.2

N1.Er.genes.3 <- N1.c0.Er[[3]] %>% select(Term, Genes, Combined.Score)
N1.Er.genes.3






```

Add some cell type annotations

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.2'

cluster.ids <- c("Glia1","RG1","Glia2","RG2","NeuronsImmature","RG3")

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$subgroups <- Idents(seu.q)

#DimPlot(seu.q, group.by = 'RNA_snn_res.0.2', label = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'subgroups', repel = TRUE)



```

```{r}
# save file
saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")

```

Main cell groups

```{r}

Idents(seu.q) <- 'RNA_snn_res.0.2'

cluster.ids <- c("RG","RG","RG","RG","NeuronsImmature","RG")

names(cluster.ids) <- levels(seu.q)
seu.q <- RenameIdents(seu.q, cluster.ids)
seu.q$Cell_Types <- Idents(seu.q)

#DimPlot(seu.q, group.by = 'RNA_snn_res.0.2', label = TRUE)
DimPlot(seu.q, reduction = "umap", label = TRUE, group.by = 'Cell_Types', repel = TRUE)

saveRDS(seu.q, "/Users/rhalenathomas/Documents/Data/scRNAseq/PhenoID/scRNAseqSorted/objs/Glia2LabledSeu03102022.RDS")

```

Proportions of cell types

```{r}

table(seu.q$Cell_Types)
dim(seu.q)

prp <- as.data.frame(table(seu.q$Cell_Types))
prp

prp$prop <- prp$Freq/sum(prp$Freq)*100
prp$Sample <- 'RadialGlia'
prp


```

I'll calculate the proportions for each cell type and make a table or plot in the comparison workbook. 



